We aimed to evaluate the association between epilepsy and suicidality, including suicidal ideation, attempts and completed suicide.Clinicaltrials.gov from 1946 to June 21, 2021 and assessed the quality of the studies using the Newcastle–Ottawa Scale. We calculated the pooled OR and the crude rate for suicidal ideation, suicide attempts and completed suicide in patients with epilepsy (PWE).We systematically searched PubMed, Embase, Cochrane Online Library, and We screened 2,786 studies and included 88 articles with 1,178,401 PWE and 6,900,657 participants as controls. Search terms included epilepsy and suicide. The pooled rates of suicidal ideation, suicide attempts and completed suicide in PWE were 19.73% (95% CI: 17.00–22.62%), 5.96% (95% CI: 4.82–7.20%), and 0.24% (95% CI: 0.11–0.42%), respectively. Compared to the control group, PWE were at a significantly higher risk of total suicidality , including suicidal ideation , suicide attempts  and completed suicide . Subgroup analyses showed significant differences in the subgroups of the measurement of suicidality.The rate of suicidal ideation, suicide attempts and completed suicide in PWE were about 19.73, 5.96, and 0.24%. And there was an increased risk of suicidality in PWE especially temporal lobe epilepsy and drug-resistant epilepsy. Clinicians need to be aware of this risk in PWE with early identification and prevention at the time of diagnosis.PROSPERO CRD42021278220.Protocol Registration:  Epilepsy is a common chronic neurological disorder that affects approximately 50 million people worldwide , and neaIn recent years, an increasing number of studies have focused on suicidality in PWE. Suicidality includes suicidal ideation, suicide attempts, and completed suicides . Most stWe performed a systematic review and meta-analysis in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) principles to investigate the association between epilepsy and suicidality. We searched the online databases of PubMed, Embase, Cochrane Online Library, and Clinicaltrials.gov.The two reviewers independently assessed the eligibility of potentially relevant studies according to the predesigned inclusion and exclusion criteria. Disputes about relevance were resolved by consensus among the investigators. The inclusion criteria were as follows.Study design: English language articles; observational or controlled studies; population: study population identified with a clinical diagnosis of epilepsy with any age; outcomes: diagnosis of suicidality through medical records, interview, or questionnaire; suicidality studied in relation to epilepsy; and outcome measures reported. Animal studies, meta-analyses, reviews, case reports and conference abstracts without full articles to evaluate the quality of studies were excluded.The two reviewers extracted data from the included studies using a data extraction form that included (1) general information: the first author, year of publication and country of study origin; (2) study characteristics: study design, number of individuals with epilepsy, number of individuals with epilepsy with suicidality, number of healthy controls, number of controls with suicidality, sexes and ages for both the epilepsy and control groups and follow-up; and (3) outcomes: the reviewers separately recorded suicidal ideation, suicide attempts and completed suicide. Suicidal ideation included suicidal risk, suicidal thoughts, suicidal tendencies, and suicidality without a defined attempt or behavior. Suicide attempts included suicidal behavior and suicide plans. In addition, the reviewers recorded the instruments used to measure suicidality and the association  between epilepsy and suicidality. If the patients from those studies were included with the same inclusion criteria in the same period and population, we extracted data from the most complete study if several studies had similar data resources or databases.Two independent evaluators assessed the quality of the studies using the Newcastle–Ottawa Scale (NOS) from three aspects: the selection of the study groups, the comparability of the groups, and the ascertainment of the outcomes of the studies. The total score was 9; a score ≥ 6 indicated high-quality literature, and a score < 6 indicated low-quality literature.via Freeman-Tukey double arcsine transformation, we performed a meta-analysis. The test level α of the effect was set to 0.05. Statistical heterogeneity was evaluated by the I2 statistic. The fixed-effects model was used for comparisons with I2 < 50%, and the random-effects model was applied for comparisons with I2 ≥ 50%. Sensitivity analysis was used to evaluate the stability of the meta-analysis results by omitting every study.All statistical analyses were performed using R 4.1.0. We calculated pooled OR in the studies with a control group for suicidal ideation, suicide attempts and completed suicide. We also calculated the crude rates for suicidal ideation, suicide attempts and completed suicide in all included studies with or without control groups. We performed a Shapiro–Wilk normality test to test whether the data fit a normal distribution. If we transformed the rate of suicidality in epilepsy We also performed prespecified analyses to evaluate subgroup differences in the pooled OR and incidence rate by country, age of the population, and measurement of suicidality. We also performed the subgroup analysis to calculate the pooled rate by specific type of epilepsy. We classified the studies into three subgroups according to different types of measurements for suicidality, including diagnostic interviews , questionnaires , and medical records . Using meta-regression, we evaluated the effects of year of publication and proportion of women in the study population on the observed associations.p < 0.0001, We screened 2,786 studies and selected 239 studies for full-text review. Following screening, we included 88 articles in our meta-analysis. A flow diagram of the article screening process according to PRISMA is shown in p < 0.0001). The funnel plot was symmetrical, and Egger’s regression test suggested limited publication bias among these studies (p = 0.08). We also found that the pooled prevalence rate of suicidal ideation in PWE in 47 studies was 19.73% . The funnel plot was symmetrical, and Egger’s regression test suggested limited publication bias among these studies (p = 0.25). Because the study designs of two articles . The funnel plot was symmetrical, and Egger’s regression test suggested limited publication bias among these studies (p = 0.50). Because the study designs of 5 articles . The rate of suicide attempts diagnosed using interviews was similarly higher to those of other measurements of suicidality . The diagnosis of completed suicide was identified by medical records. The pooled prevalence rate of suicidal ideation in NDE, TLE and DRE were, respectively, 0.12, 0.27, and 0.29. And the pooled prevalence rate of suicidal attempts in NDE, TLE and DRE were, respectively, 0.02, 0.13, and 0.16.The results of prespecified subgroup analyses and meta-regression are shown in p = 0.0342) was a significant moderator that contributed to heterogeneity between the studies in the risk of suicidality. The year of publication and the percentage of females may not contribute to the heterogeneity between studies (p > 0.05). The sensitivity analyses showed that the results of the meta-analysis were stable , including suicidal ideation (2.70 times), suicide attempts (2.74 times) and completed suicide (2.36 times). The rates of suicidal ideation, suicide attempts and completed suicide in PWE were 19.73, 5.96, and 0.24%, respectively. This result is consistent with previous studies, which concluded that there was a clear relationship between epilepsy and suicide attempts and completed suicide , 25. Pre.60 timesIn the subgroup analysis, we found that the different diagnoses may not have influence on the relative risk of suicidality but affected the rate of suicidality, including suicidal ideation and attempts. We found that the rate of suicidality diagnosed by interviews was higher than that diagnosed by questionnaires and medical records. A lack of enough knowledge to seek medical attention in time, a lack of diagnosis from doctors or a lack of medical resources may cause the lower rate of suicidality identified by medical records. This result suggested that using medical records to diagnose suicide may underestimate suicidal rates. Analyses in different types of epilepsy found that there was a higher rate of suicide in DRE and TLE. The suicide rates reported for people with TLE were 6–25%, compared with 1.4–6.9% in the general population . PatientSubgroup analyses found that the rate of suicidal ideation in developing countries was significantly higher than that in developed countries, consistent with the findings of the WHO World Mental Health Surveys . The WHOThe relationship between epilepsy and suicidality is complex, and epilepsy is a frequently misunderstood and highly stigmatized condition. Epilepsy affects relationships with family and friends, employment, school, and leisure activities and results in social and economic consequences , 29. It The mechanism of suicidal behavior in PWE is still uncertain. That may suggest the possible common neurobiological pathogenic mechanisms in suicidality and epilepsy including disturbances of several neurotransmitters and the hypothalamic pituitary adrenal axis . FurtherThe present study had several limitations. First, our included studies had obvious heterogeneity, even after subgroup analysis and meta-regression. This may be due to the different designs, sample sizes, age, sex, duration of epilepsy, comorbid substance use, comorbid depression and anxiety etc. Due to obvious heterogeneity, we cannot specify the pooled prevalence in the meta analysis as the lifetime prevalence or 1-year prevalence. Second, most of the included studies were retrospective, which might have limited the power of our analysis to measure significant differences in outcomes. Similarly, recall bias cannot be excluded. We only searched for articles in English with limited search terms, “epilepsy,” and “suicide”; therefore, several studies may be ignored. Third, the diagnostic tools for assessing suicidality and populations differed among the studies, which may contribute to the heterogeneity. Fourth, there are few articles about specific types of epilepsy in children, which need to further explore in future studies.This systematic review and meta-analysis found that PWE are at an increased risk of suicidality, including suicidal ideation, suicide attempts and completed suicide. The rate of suicidal ideation, suicide attempts and completed suicide in PWE were about 19.73, 5.96, and 0.24%. In view of the high risk of suicidality in PWE, it is important for clinicians to pay more attention and provide essential screening for early identification and prevention at the time of diagnosis. For especially TLE and DRE, it is necessary to screen by Mini International Neuropsychiatric Interview which can reduce the omission diagnostic rate.The original contributions presented in the study are included in the article/HW and YZ was responsible for the search, data acquisition and interpretation, and drafting the manuscript. HW performed the meta-analysis and generated the figures. YZ was responsible for the study conception and drafting the tables. GT, DC, and YF was responsible for study design and checking the data. LL revised the manuscript and provided the study funding. All coauthors reviewed the final version. All authors had full access to all the data in the study and final responsibility for the decision to submit for publication.This work was supported by Health Commission of Sichuan Province (grant number 20ZD005) and Science & Technology Department of Sichuan Province (grant number 2021YFS0174).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Solo deep learning (SDL) and ensemble deep learning (EDL) models are not robust enough. Recently, attention mechanisms have been introduced in SDL. We hypothesize that attention-enabled EDL (aeEDL) architectures are superior compared to attention-not-enabled SDL (aneSDL) or aeSDL models. We designed EDL-based architectures with attention blocks to build eleven kinds of SDL model and five kinds of EDL model on four domain-specific datasets. We scientifically validated our models by comparing “seen” and “unseen” paradigms (SUP). We benchmarked our results against the SemEval (2016) sentimental dataset and established reliability tests. The mean increase in accuracy for EDL over their corresponding SDL components was 4.49%. Regarding the effect of attention block, the increase in the mean accuracy (AUC) of aeSDL over aneSDL was 2.58% (1.73%), and the increase in the mean accuracy (AUC) of aeEDL over aneEDL was 2.76% (2.80%). When comparing EDL vs. SDL for non-attention and attention, the mean aneEDL was greater than aneSDL by 4.82% (3.71%), and the mean aeEDL was greater than aeSDL by 5.06% (4.81%). For the benchmarking dataset , the best-performing aeEDL model (ALBERT+BERT-BiLSTM) was superior to the best aeSDL (BERT-BiLSTM) model by 3.86%. Our scientific validation and robust design showed a difference of only 2.7% in SUP, thereby meeting the regulatory constraints. We validated all our hypotheses and further demonstrated that aeEDL is a very effective and generalized method for detecting symptoms of depression in cross-domain settings.Depression is increasingly prevalent, leading to higher suicide risk. Depression detection and sentimental analysis of text inputs in  Depression is a serious and debilitating mental health condition that affects millions of people worldwide, affecting 260 million people globally . AccordiDepression detection has been conducted for over 200 years through the identification of an individual’s behavior by qualified psychologists ,11. MachDeep learning (DL) has rapidly gained momentum in large number of applications due to its automated ability to extract automated features . These mattention mechanism could potentially improve the accuracy, reliability, and generalizability of the model, particularly in applications related to mental health or other complex domains. Study  be the input sequence of length n, and let H =  be the output sequence of the multi-head self-attention layer, where hi is the representation of the ith element in H.Let X = [xks is the dimensionality of the query. Then, we calculate the attention scores using the SoftMax function s and multiply it with V to obtain the weighted sum for each head:First, we compute Query (Q), Key (K), and Value Vectors (V), which are learnable parameters, and dThen, we compute the output sequence using the concatenation of each head, where © denotes the concatenation function: os from H, where it is the desirable size for the next layer: a is a learnable weight matrix for mapping the output of the layer to the desired output size.Lastly, we apply a linear transformation to map the output and obtain the desired output size  Hbce, and mathematically, it can be expressed as:i is the ground truth label (either 0 or 1), The models in the study were trained using a batch size of 128 and an input layer of 100 tokens, with a binary cross-entropy loss (CLE) function. Binary CLE is a loss function used in ML for binary classification problems. Cross-entropy is a mathematical function that is defined in terms of the logarithm of the predicted label and the gold standard label. It measures the difference between the predicted probabilities of the positive class and the true labels, and penalizes the model for large errors. The binary cross-entropy loss function is denoted as L5 and for EDL4 and EDL 5, it was 1 × 104. Finally, it should be noted that EDL 1 was trained for 30 epochs, EDL 2 and EDL 3 were trained for 40 epochs, EDL 4 was trained for 45 epochs, and EDL 5 was trained for 50 epochs. The study was implemented using Python 3.8 and a TensorFlow framework. To implement the system, a 12 GB NVIDIA P100 16 Graphics Processing Unit (GPU) was utilized. Additionally, the system was equipped with an Intel Xeon Processors processor and 12 GB of RAM.Based on our preliminary analysis and introduction, we developed an experimental workflow, which is outlined in this section. Initially, we examined the SDL models and compared the advantages of bidirectional models over unidirectional models. Subsequently, we investigated how combining SDL models with EDL models can enhance performance on standard datasets. We then evaluated the impact of adding an attention layer to these models on the overall performance of the depression and sentiment analysis task. Finally, we cross-validated our observed results and demonstrated the domain adaptability of our system by performing an unseen paradigm (a situation where the deep learning model is tested on a new and previously unseen task or dataset that is significantly different from the data it was trained upon).We conducted this experiment on the SDL model, comparing the performance metrics of unidirectional models versus their bidirectional counterparts  averaged across our four main datasets  to visualize how the bidirectional model fared compared to the unidirectional model under a constant K10 partition protocol.The aim of this experiment was to determine whether the EDL models are superior to their corresponding SDL models, averaged across our four main datasets . For this experiment, we compared five EDL models and seven SDL models to demonstrate how joining multiple SDL models can improve performance under a constant K10 partition protocol.To validate the robustness of the models, we applied four cross-validation protocols, namely, K2, K4, K5, and K10, to vary the training size for each model and evaluate the corresponding performance drop resulting from reducing the training size. We utilized these partition protocols across the four main datasets  and averaged the results to illustrate how data size affects our model.The purpose of this experiment was to observe the change in performance of the SDL and EDL models when augmented with a self-attention block after the classifier in the architecture, compare them with the original EDL model across all datasets , and benchmark them under a constant partition protocol.This experiment was one of the most critical, as it aimed to evaluate the EDL model’s performance when encountering cross-domain data (where a model was trained on one domain and was then applied to test a different domain) using an “unseen test dataset”. Specifically, we trained the model on one dataset and evaluated it on a different dataset with varying domains and semantics to demonstrate its generalization ability. Our model showcased domain adaptation following training on a single domain of sentiment data and an evaluation of its ability to adapt to a new domain by testing its performance on depression data. We were able to transfer knowledge learned from the original domain to a new domain and assess the model’s ability to generalize to different tasks and datasets.Accuracy: This denotes the overall correct predictions out of the total predictions made (Equation (5)). (ii) Recall (R): This is the number of correctly predicted positive class predictions made of all the positive members in the dataset (Equation (6)). (iii) Precision (P): This is the number of correctly predicted positive class predictions to the total number of classified positive predictions (Equation (7)). (iv) F1-Score (F): This is defined as the harmonic mean of precision and recall. It is useful for imbalanced datasets (Equation (8)). (v) Finally, the area-under-the-curve (AUC) represents the two-dimensional area underneath the plotted ROC curve.The proposed models were estimated using the parameters “true positive (TP)”, “true negative (TN)”, “false positive (FP)”, and “false negative (FN)”, which are defined as follows: If a normal/neutral sentiment input is detected as a normal/neutral sentiment by the depression detection mechanism, then it is identified as true positive (TP). If a depressive/negative sentiment input is detected as a depressive/negative sentiment by the depression detection mechanism, then it is identified as true negative (TN). In the other case, if a depressive/negative input is detected as a normal/neutral sentiment by the mechanism, then it is identified as false positive (FP). Finally, if a normal/neutral input is detected as a depressive/negative sentiment by the mechanism, then it is identified as false negative (FN). Using these parameters, we can derive the following PE parameters: (i) In this study, we propose formulations for measuring the overall robustness of the model. To accomplishThe experimental results of the protocols were obtained by employing four main datasets  and sixteen (11 + 5) models through the utilization of the TensorFlow framework. The training process was executed using a Tesla P100 GPU. Each result was obtained by conducting ten rounds of training and testing, and subsequently, calculating the mean value.In this study, we demonstrated that bidirectional models consistently outperform unidirectional DL models of comparable architecture. We tested this hypothesis by training and testing six baseline models, with both bidirectional and unidirectional variations. Across all three variations, the bidirectional models consistently outperformed the unidirectional models, as visualized in 4.49%. Moreover, the best increase in EDL model performance was observed in EDL4, which showed a 7.22% absolute increase over its component, SDL8. These results demonstrate the effectiveness of EDL models in improving the overall performance of DL models. This experiment demonstrates how EDL models outperform their individual components. We evaluated eleven SDL models and five EDL models, and their performance metrics were averaged over four datasets; the results are presented in This could be attributed to the fact that EDL models are able to leverage the strengths of different models and overcome their weaknesses, leading to improved accuracy and generalization ability. In contrast, SDL models may struggle to capture complex relationships in the data or be prone to overfitting. EDL models address these limitations by combining the predictions of multiple models, each with different strengths and weaknesses, thereby reducing the risk of overfitting and improving the robustness of the model.95.01% when using the K10 protocol to 90.07% when using the K2 protocol, and the AUC fell from 0.9251 when using the K10 protocol to 0.8667 when using the K2 protocol. Even with a reduced amount of training data in the K2 (50:50) validation protocol, the metrics of our EDL models did not drop significantly, demonstrating the generalizability of the models. These results suggest that our EDL models can be used effectively even when the amount of available training data is limited.In this experiment, we studied the effect of training data size on the performance of our models. aeSDL > aneSDL for all five of the PE metrics, and similarly, the mean accuracy of aeEDL > aneEDL for all five of the PE metrics. This further proves our hypothesis that “attention blocks” are a powerful paradigm in depression and sentimental analysis.The fourth experiment aimed to demonstrate the effect of using an attention layer in all of the SDL and EDL models. For this purpose, we implemented a self-attention channel on top of the EDL models. As demonstrated by 85.09% and an AUC score of 0.8008; this is the highest accuracy achieved so far using EDL5 with the self-attention block, giving an boost in accuracy of 3.86%, compared to the best score for SDL11 with the self-attention block. These results, shown in Furthermore, with this experiment, we were able to establish a benchmark on the SemEval 2016 Subtask A dataset, with an accuracy of ~3%. Similarly, the mean difference between unseen and seen accuracy for the EDL models was ~2.7%. In this experiment, we demonstrate our model’s ability to perform in a cross-domain setting by conducting unseen tests. We performed 12 sub-experiments on four datasets, involving training on one dataset and testing on a different one, covering all possible combinations. The performance results were averaged out for all the datasets, and the accuracy and percentage differences in seen accuracy and unseen accuracy are shown in ~3%. Similarly, the mean difference between the unseen and seen AUC for the EDL models was ~2.4%. Note that the criterion for a robust design, leading to superior generalizability, was that the difference between seen and unseen analysis be less than 3% to 5% [3%, which qualifies it as a robust, generalizable, and stable design, which is also part of our running hypothesis.The corresponding AUC and percentage differences are shown in 3% to 5% ,55,56; ot-test. The statistical tests were used to determine whether the differences in performance between the models are significant.As part of the performance evaluation, the classifiers of the models were determined through their ROC curves, and bar charts were plotted to visualize the performance of the models. ROC curves and bar charts provide a visual representation of the model’s performance. Overall, the performance evaluation provides insight into the strengths and weaknesses of the system and helps to identify areas for improvement. The reliability of the system was assessed to determine its robustness and the stability of the model. This was achieved through various statistical tests, such as the R-squared test (adjusted), and paired As part of the increasing interpretability of the AI models, explainable AI techniques were employed. These techniques provide insights into how the black-box models make decisions and help understand the factors contributing to depression detection.0.9251, and the AUC for K2 is 0.8867. The ROC performance of the five EDL models  is visualized in 0.9251 and EDL1 having the lowest AUC score of 0.8616.ROC curves are used to evaluate the performance of the models across their entire been operating range. In 87.63% and EDL5 having an accuracy of 95.01%. 90.07% from 95.01% in K10 (default).The bar charts are helpful in visualizing the information present in tables more efficiently. t-test, and ANOVA test. These tests were conducted to determine whether the predicted data were significant and to monitor the p-value in the paired t-test and ANOVA test to check whether it was less than 0.01 (p < 0.001). The results of these tests are presented in The stability of the system was validated through four statistical tests conducted on the EDL models across all five datasets. The tests performed were the adjusted R-squared test, two-tailed Z test, paired p < 0.001 in the paired t-test and the ANOVA test, signifying the significance of the data and validating their clinical importance. The adjusted R-squared test, which portrays the correctness of the model, illustrates the extent of a feature’s variance, and the Z in two-tailed tests denotes the Z-score, which describes the standard deviation above or below the mean population.Along the lines of , we condGiven that deep learning models, such as BERT, are often considered black box models, we recognized the importance of providing insights into the interpretability of our results. To address this, we employed the “SequenceClassificationExplainer” module from the “transformers-interpret” library in our paradigm, as showcased in In our study, a value closer to 0 indicates a depressive sentiment, while a value closer to 1 indicates a non-depressive sentiment. By incorporating this explainable AI technique, our aim was to shed light on the underlying factors influencing sentiment classification. The results over the two datasets are demonstrated in aeEDL and aeSDL) against the existing studies in the domains of depression and sentimental analysis. Another important component is to elaborate on the bounds of the attention mechanism under which it was adapted. Lastly, as part of the discussion, we illustrate the strengths and weaknesses and possible extensions of this study.Since we implemented 11 SDL and 5 EDL models on four datasets with and without attention paradigms (16 × 2 = 32 models), we summarize the primary and secondary findings of our comprehensive analysis. Further, it is critical to benchmark our design , we discovered that bidirectional SDL models outperformed unidirectional models. Building upon this finding, we discovered that EDL models outperformed their component SDL models by 4.49%, and yielded better results when they were utilized together in the architecture. Using self-attention layers, we observed significant improvement in the performance of DL models. This was further enhanced by incorporating attention mechanisms into our EDL architecture, leading to benchmark accuracy on the SemEval-2016 dataset. We observed that the increase in the mean accuracy (AUC) of aeSDL over aneSDL was 2.58% (1.73%), and the increase in the mean accuracy (AUC) of aeEDL over aneEDL was 2.76% (2.80%). When comparing EDL vs. SDL for non-attention and attention, the mean aneEDL was greater than aneSDL by 4.82% (3.71%), and the mean aeEDL was greater than aeSDL by 5.06% (4.81%). On benchmarking dataset , the best-performing aeEDL model (ALBERT + BERT-BiLSTM) was superior to the best aeSDL (BERT-BiLSTM) model by 3.86%. Furthermore, we validated our models through statistical tests, demonstrating their ability to effectively handle cross-domain challenges by performing well on unseen paradigms and predicting on different domains to those on which they were trained. We met the regulatory requirement by showing that the accuracy and AUC differences between unseen and seen paradigms were less than 3%.Through an exhaustive study, we have proven attention-enabled paradigm in EDL models. These EDL models were designed by fusing DL-based models. Thus, it is important to evaluate our framework against the previous SDL and EDL models. We therefore decided to squarely address the benchmarking efforts in two consecutive steps, with step one involving a comparison of our proposed models with previous DL models and step two consisting of a deeper comparison of our proposed models with previous EDL models. Since the total number of studies in the sentiment analysis and depression detection were 9 and 18, respectively, we organized our benchmarking into two clusters in the form of two tables, namely, The crux of our study was positioned using an Out of the nine studies, six studies ,61,62,6391.44% across all EDL models and 95.01% in the best EDL5 model, and an F1-Score of 0.8941. Seven out of the nine studies [p-values of less than 0.001, or by performing cross-validation , unlike in our proposed study (R19), where we conducted exhaustive tests, including six individual statistical tests that yielded p-values of less than 0.001, deployed four cross-validation protocols, and achieved an overall standard deviation of less than 2.5%. Further, it is noted that only our proposed work (R10) conducted a true unseen paradigm (column C14) by training and testing the model on datasets of different domains, thus proving its generalizability over cross-domains.The studies demonstrated up to four kinds of data source, namely, social media chat, reviews, or published psychological data (column C8). Unlike these, in our proposed study (R10), keeping generalizability in mind, we used five kinds of data source, namely, Twitter, Twitter (Stanford), Reddit, Kaggle, and SemEval (for benchmarking). Furthermore, these nine studies had a data size range of 718 to 550,000 sentences (column C9), while in our proposed study (R10), the data size ranged from 10,000 to 1,000,000 sentences. All of these studies computed at least one out of accuracy, F1-Score, or AUC . Our study (R10) outperformed existing studies and yielded a mean accuracy of  studies ,63,64,65Most of the studies worked on sentiment analysis ,78,79,8093.97% and an F1-Score of 0.9192. Furthermore, our study provided benchmark accuracy over the benchmark public SemEval-2016  dataset, achieving 85.09% accuracy on our aeEDL model (ALBERT + BERT-BiLSTM).Ten out of the eighteen studies ,80,83,84p-values of less than 0.001, or performing cross-validation , unlike in our proposed study (R19); we conducted six individual statistical tests that yielded p-values of less than 0.001, deployed four cross-validation protocols, and achieved an overall standard deviation of less than 2.5%. While only three studies attempted to validate unseen data [Studies ,80,82,83een data ,83,84 byAttention mechanisms help in depression detection by allowing the model to selectively focus on important parts of the input text, instead of treating the entire text equally. This is particularly useful in depression detection, where certain words or phrases may be more indicative of depression in some individuals than in others. For example, an attention mechanism could help the model to identify important keywords or phrases that are highly indicative of depression, such as negative self-talk, hopelessness, or social isolation. relevant information and ignoring irrelevant information. This can lead to improved accuracy and performance of the model in identifying depression in text data.In depression detection, certain words or phrases may carry more weight than others, and attention mechanisms can help the model identify and prioritize these important features. Additionally, attention mechanisms can help the model better understand the context and meaning of the text by focusing on This article focuses on the application of EDL models and attention layer for depression detection. The study shows significant improvement in predicting sentiment and depression from multiple data sources, making the proposed EDL a benchmark in the field of depression detection. The EDL model outperforms existing studies on two datasets. Additionally, cross-validation, clinical validation, and unseen implementations prove the system’s robustness and domain adaptability, as it performs fairly well on a different domain to that on which it was trained, demonstrating its generalization ability.Due to the limited availability of high-quality open datasets on depression, the existing study focused mainly on training classifiers on specific datasets. Consequently, the model’s accuracy was not improved beyond 95%, although it still outperformed existing studies. To encourage further research in the field and improve current benchmark models, high-quality datasets of substantial size are necessary to build more robust and optimal models. The scope of this approach focuses only on the NLP text-based approach, and hence, we adapted the Twitter and Reddit dataset as they follow the same paradigm. This was an in-depth explanation of the domain adaption paradigm, which focused on the adaption of ensemble-based NLP models through extensive experimentation. We developed 11 solo deep learning models and 5 ensemble models, which were specifically designed to leverage their capabilities in detecting depression from users’ text patterns. Additionally, within our ensemble models, we incorporated attention channels to enhance explainability, highlighting key textual features that contribute to the classification decision. By employing these techniques, we aim to achieve both high performance in depression detection and meaningful explanations for the model’s decisions, ensuring transparency and interpretability in our classification approach.The exploration of multimodal videos and images will be considered as a potential continuation in future research. Through such research, we can explore datasets from visual-based social media platforms such as TikTok, YouTube, and Instagram. Here, we will shift our focus to video classification, which requires different methodologies compared to the NLP-based classification we have utilized thus far. We will employ computer vision-based classification models to analyze visual cues, facial expressions, body language, and other visual elements in order to detect depression using an entirely different paradigm.aeEDL models, as adopted in the imaging framework, to increase their robustness and improve their performance metrics [In the future, our goal is to develop new datasets and explore novel architectures, such as Generative Adversarial Networks (GANs), for improving depression detection. We aim to compare these new models, such as the fusion of ML with exhaustive feature space with DL , to our  metrics ,88. Addi metrics , and art metrics ,91,92,93Lastly, there have been studies in different domains, such as immunology ,95, cardThe proposed cloud system follows a layered architecture, where the presentation layer operates locally on users’ devices, while the business and persistence layers are hosted on the cloud. This architecture ensures a user-friendly experience by providing real-time sentiment analysis and emotional guidance directly on the device. Additionally, this architecture facilitates secure connectivity between the system and psychologists, enabling them to access and utilize the system’s insights to provide personalized support and assistance to their patients. The automated moderation provided by this system can greatly benefit psychologists in their practice. When patients visit a psychologist, it can sometimes be challenging for them to express their emotions fully. With the assistance of our automated system, psychologists can gain deeper insights into their patients’ emotional well-being. This enhanced understanding will enable psychologists to provide more personalized and tailored treatment plans, improving the effectiveness of their interventions. By utilizing this system, psychologists can leverage technology to follow up with their patients’ mental well-being in a more comprehensive and individualized manner.The cloud-based nature of our system will play a crucial role in its capabilities. It will enable the secure storage and processing of a vast amount of data, allowing the system to continuously learn and enhance its understanding of emotions. This accumulated knowledge and analysis of sentiment data contribute to a more robust and accurate sentiment analysis process. Furthermore, the integration of this system into mobile phone-based applications will provide users with convenient access to its features. Users can benefit from real-time guidance and support, empowering them to manage their emotions and prioritize their mental well-being more effectively. This sentiment analysis system will act as a personal mental guide in a robust pipeline, operating locally to help users recognize and address their emotions. Additionally, the integration of AI sentiment analysis could enable holistic support for mental well-being, positively impacting individuals’ lives by providing timely assistance and resources based on their emotional needs. Our study presents a novel paradigm for depression detection and sentimental analysis in a cross-domain framework based on text inputs. This utilizes five kinds of attention-enabled ensemble deep learning model designed using eleven kinds of solo deep learning model. A comprehensive data analysis was conducted using four kinds of dataset to prove our hypothesis. Further, a benchmarking strategy was developed on the standardized SemEval dataset, establishing our model’s superior performance both in classification accuracy and area-under-the-curve. As part of a generalizability assessment, “seen” and “unseen” experiments were conducted, with the model meeting the regulatory requirements. Finally, the system’s reliability and stability were demonstrated using clinical tests.


Preventing and reducing violence is of high importance for both individuals and society. However, the overall efficacy of current treatment interventions aimed at reducing aggressive behavior is limited. New technological-based interventions may enhance treatment outcomes, for instance by facilitating out-of-session practice and providing just-in-time support. Therefore, the aim of this study was to assess the effects of the Sense-IT biocueing app as an addition to aggression regulation therapy (ART) on interoceptive awareness, emotion regulation, and aggressive behavior among forensic outpatients.A combination of methods was used. Quantitatively, a pretest-posttest design was applied to explore group changes in aggression, emotion regulation, and anger bodily sensations associated with the combination of biocueing intervention and ART. Measures were assessed at pretest, after 4 weeks posttest, and after one-month follow-up. During the 4 weeks, a single-case experimental ABA design was applied for each participant. Biocueing was added in the intervention phase. During all phases anger, aggressive thoughts, aggressive behavior, behavioral control, and physical tension were assessed twice a day, and heart rate was measured continuously. Qualitative information regarding interoceptive awareness, coping, and aggression was collected at posttest. 25 forensic outpatients participated.A significant decrease in self-reported aggression was found between pre- and posttest. Furthermore, three-quarters of participants reported increased interoceptive awareness associated with the biocueing intervention. However, the repeated ambulatory measurements of the single-case experimental designs (SCEDs) did not indicate a clear effect favoring the addition of biocueing. On group level, no significant effects were found. On the individual level, effects favoring the intervention were only found for two participants. Overall, effect sizes were small.Biocueing seems a helpful addition to increase interoceptive awareness among forensic outpatients. However, not all patients benefit from the current intervention and, more specifically, from its behavioral support component aimed at enhancing emotion regulation. Future studies should therefore focus on increasing usability, tailoring the intervention to individual needs, and on integration into therapy. Individual characteristics associated with effective support by a biocueing intervention should be further investigated, as the use of personalized and technological-based treatment interventions is expected to increase in the coming years. For this purpose, several treatment interventions have been developed over the last decades. Most of these interventions are based on cognitive behavioral therapeutic principles and share elements with Aggression Replacement Training : a treatOver the last years, the number of studies focusing on the psychophysiological correlates of antisocial spectrum behavior and aggression has increased . In aggrPsychophysiological measures are not only used to understand aggressive behavior but can also be used to predict aggressive incidents in real life. For a long time most studies aimed at identifying these physiological biomarkers were conducted in laboratory settings . HoweverSome of the aforementioned challenges in treatment of forensic outpatients with aggressive behavior, such as the difficulties in recognizing physiological signals that precede aggressive incidents and the limitations in out-of-session practice, might be addressed by implementing the psychophysiological research results facilitated by the fast developments in e- and m-health technology. New interventions, such as serious gaming , virtualThis new intervention, biocueing, can be considered a derivate of traditional biofeedback, in which users are provided with real-time physiological information and trained to influence physiological parameters, such as HRV  or cardiGiven the potential of biocueing for the forensic population, we investigated the acceptability, usability, and clinical changes associated with the use of an earlier version of the Sense-IT biocueing app  in a two2.2.1.1), was followed by an intervention phase (B) and a follow-up phase (A2). In the two-week intervention phase, biocueing was added. Initially, we planned to randomize the start of the B-phase to either 5, 7, or 9 days after the start of phase A1 for each group of three participants. However, since this procedure could not be aligned to the routines and schedules of potential participants, we had to let go of this multiple baseline aspect of the design. During all phases, the emotional state of the participants was assessed twice a day and HR was continuously measured. Qualitative data was collected at T1 via semi-structured interviews, enabling us to obtain a deeper understanding of patients’ experiences concerning the effectivity of the Sense-IT. The study protocol and subsequent amendments were approved by the Medical Ethical Committee of Amsterdam University Medical Centre, Vrije Universiteit, Netherlands (NL63911.029.17). The study was registered in Netherlands Trial Register (NL8206).In this study, we used a combination of methods to answer the research question. Forensic outpatients receiving ART were invited to use the Sense-IT app , 2019 fo2.2.Forensic outpatients, receiving ART at Inforsa, a forensic mental healthcare facility in the Netherlands, were recruited for participation in this study from 2020 to 2022. Potential participants were screened for eligibility by a research associate, consulting the patients’ therapist. The eligibility criteria included: (1) a proven lack of anger management skills, indicated by either a recently committed violent crime and/or a high risk of committing one; (2) assignment to individual outpatient ART after multidisciplinary consultation; (3) basic understanding of mobile applications; and (4) an age of 16 years or above. The exclusion criteria included: (1) acute manic or psychotic symptoms; (2) current high risk of suicide; (3) severe addiction problems or other severe conditions requiring immediate intervention or hospitalization; and (4) insufficient understanding of the Dutch language. The first two inclusion criteria were assessed by checking the committed index defense (if applicable) and the clinical decisions recorded in the electronic patient file. The first three exclusion criteria were assessed by consulting the patients’ therapist, using cut-off scores on the corresponding items of the Health of the Nations Outcome Scales HoNOS; . After s2.3.2). In these short appointments questions were answered, if applicable, and participants were reminded to answer the daily questions. After these 4 weeks, another 60 min assessment (T1) was planned, in which both qualitative and quantitative measures were administered. One month after T1-assessment, a 30 min follow-up assessment (T2) was scheduled. An overview of outcomes and their moment of assessment is presented in Eligible patients expressing interest in the research project received a face-to-face appointment in the presence of their therapist to discuss study participation. The research associate provided the patient with a brief oral description and full written information about the study. The voluntary nature and the absence of any negative consequences refusing participation were emphasized. When the patient expressed willingness to participate, the next appointment was planned after at least 7 days, providing enough time for consideration. In this appointment written informed consent was obtained and baseline measurement (T0) was administered, which lasted approximately 60 min. After completion of the questionnaires, participants were provided with a smartwatch and mobile phone with the Sense-IT app. Participants were shown how to use the devices and were advised on charging and using the system safely. They also received a user manual. Participants used the devices independently during the following 4 weeks. They were encouraged to call the research associates if any problem occurred. During these weeks, the research associate met with the participants twice; once to start the intervention phase (B) and once to start the follow-up phase  and the SCEDs . Finally, we describe the qualitative measures.2.4.1.via Bluetooth. The Sense-IT system reads the physiological data measured by the photoplethysmography (PPG) sensor and stores the data in a local database on the smartphone itself. The build-in algorithm compares the current HR to the user’s mean HR at baseline and calculates a level between −3 and 5 using the standard deviation of the baseline measurement. In the current study, we further refined the baseline measurement procedure. Ultimately, baseline measurement was performed during T0, included at least 1 min of sitting in quiet, 1 min of social interaction, and 1 min of walking activity to account for sufficient variation, and lasted until the PPG sensor received 500 reliable HR measures. Our starting values for HR and heart rate variance thresholds were in line with published norms indicating a mean HR around 80 , was preinstalled on all smartwatches and mobile phones before distribution. The Sense-IT app was originally developed by the University of Twente and Scelta, an expert center for psychiatric patients with personality disorders , 2019 an SD ~ 7; . More in2.4.2.2.4.2.1.2.4.2.1.1.At T0, T1 and T2 primary and secondary measures were administered to explore relevant changes on group level. The Aggression Questionnaire-Short Form AQ-SF; , a 12-it2.4.2.1.2.At T0, several other secondary measures were administered to describe the sample. A self-developed demographic questionnaire was used to gather information regarding age, gender, ethnicity, education, and past offenses. The most recent DSM-5 main psychiatric diagnosis for each participant was retrieved from the electronic patient record. To gain a better understanding of the type and nature of aggressive and antisocial behavior, three other self-report measures were administered at baseline: the Reactive Proactive Questionnaire RPQ; , Youth P2.4.2.2.2.4.2.2.1.During the 4 weeks of the ABA designs, Ecological Momentary Assessment (EMA) was used to assess the emotional state of the participants. For this reason, six questions were designed based on the items of the Positive and Negative Affect Schedule PANAS; . Partici2.4.2.2.2.1 and A2).During the 4 weeks of the ABA designs, HR was continuously measured while the Sense-IT app was used. The Sense-IT app also registered the baseline settings, kept track of the levels  and the activity profiles , and whether biocueing was active (phase B) or not  were analyzed using SPSS . After checking the normality assumptions for main scales and subscales and given the small sample size (particularly for the comparisons with T2), we decided to use the nonparametric equivalent of the paired 2.5.1.2.1, B, and A2), using the track record of the Sense-IT app. For EMA, responses were considered as belonging to the last preceding prompt, unless the response was given less than 30 min before the next prompt. In case of multiple responses within 30 min, the response that deviated the most from the specified prompting time was discarded. In case of phase ambiguities, EMA responses were assigned to the phase to which the majority (>50%) of the period over which they reported  belonged. For HR, measurements with specific activity profiles  were disregarded from the measurements to focus on HR data in no  to limited  movement scenarios. Furthermore, the HR data was corrected for very low and high values (< 50 bpm and > 190 bpm). To calculate mean and standard deviation per day part, HR data was split into daytime (08:00 AM – 04:59 PM) and evening measures (05:00 PM - 01:59 AM). A day part was considered missing when less than 500 HR measures were present. When participants had no access to the Sense-IT app and its associated devices for at least three days , the corresponding period was not included in the analysis.In order to analyze the SCED data, all EMA and HR measures were divided into the three phases (Ap ≤ 0.01) by dividing the critical value of p (α = 0.05) by the number of comparisons (five). In addition, we assessed effect sizes on group and individual level for the EMA variables for which we expected a specific direction of change  by calculating the Improvement Rate Index  independently ranked the three most informative textual responses regarding interoceptive awareness, use of coping strategies, and prevention of aggressive incidents. The final quotations were selected by discussion between two researchers (JtH and LS), until consensus was reached, and translated from Dutch into English.3.3.1.The majority of the 25 forensic outpatients who participated in this study was male (92%) and born in Netherlands (92%). For most of them, treatment was a mandatory part of their conditional sentence (64%), mainly imposed because of a violent index offense (94%). A large proportion (73%) reported problems in the family of origin: domestic violence and substance abuse were most frequently reported, but criminal behavior and psychological problems were also mentioned. All descriptive characteristics are summarized in 3.2.Z = −2.043, p = 0.041. No significant decreases in aggression were found between the other moments of assessment. For therapist reported aggression level, emotion regulation difficulties, and insight in anger bodily sensations no significant differences were found between pre-, post- and follow-up assessment. For this sample, three of the outcome measures changed in the expected direction between T0 and T1, and one measure (anger bodily sensations) changed in the opposite direction.First, we analyzed the results of the quasi-experimental designs with pre-, post-, and follow-up measurements. The mean scores and standard deviations on clinical outcomes aggression (AQ-SF and MOAS), insight in anger bodily sensations (ABSQ), and emotion regulation difficulties (DERS), for each moment of assessment, are presented in 3.3.1 and seven participants stopped using the app after phase B. The compliance to EMA, defined as the ratio of the number of completed EMA questions in relation to the total number of EMA prompts per phase, ranged from 43.7% in Phase A1, to 24.7% in Phase B and 16.0% in Phase A2. For EMA, only 3 participants met the criterion of at least 5 data points for all phases. The compliance to HR measurement, the ratio of available daytime or evening measures in relation to the maximum amount of these measures per phase, ranged from 38.5% in Phase A1, to 29.9% in Phase B and 13.5% in Phase A2. For HR, none of the participants had sufficient measurements in all phases. Therefore, only the results of the baseline phase (A1) and intervention phase (B) were used for further analysis: for EMA, 9 participants had sufficient data in phase A1 and B; for HR this applied to 10 participants.Next, we analyzed the results of the ABA designs. In order to select the participants with sufficient data points, we started by investigating data availability. One participant did not start using the Sense-IT app, one participant quit after phase AFirst, we applied a multilevel piecewise regression approach (two-level) and visual analyses to analyze the effects between phases for five EMA variables and two HR variables on group level, using the data of all eligible participants. We inspected B1 (for both baseline and intervention phase), B2 and B3. On group level, we found no significant developmental effects (neither for baseline nor for intervention phase), no significant immediate changes when transitioning into the intervention phase, and no significant interaction effects on any of the variables. All group-level parameter estimates are presented in the Subsequently, piecewise regression analyses and visual analyses were conducted for each of the eligible participants separately (one-level), using MultiSCED. Significant developmental effects (for both baseline and intervention phase), immediate changes when transitioning into the intervention phase, and interaction effects are reported in the Improvement rate differences (IRDs) for the two primary EMA outcomes on the individual level ranged from 0.05 to 0.51 for aggressive behavior (decreasing direction) and from.05 to.55 for behavioral control (increasing direction), indicating small effect sizes with some exceptions to moderate .Finally, we zoomed in on the three participants in which we found visually interesting patterns and significant interaction effects to enhance clinical understanding of the results. The names of the participants are fictitious.1). Most remarkable in the intervention phase (B) is the increase in aggressive thoughts. Both the decrease in aggressive thoughts during the baseline phase and the increase in the intervention phase reached statistical significance. However, Ryan reported that he did not express these thoughts in aggressive behavior, as indicated by the flat line. When the patterns in both phases were compared, his outcomes regarding aggressive thoughts were significantly in favor of the baseline phase. No significant differences between phases were found on other variables. At post-test (T1) Ryan reported a substantially lower score on the AQ-SF (6th decile) compared to baseline, but a higher score on the DERS. He reported that the Sense-IT biocueing app did not work for him. He noticed no effect of biocueing on his awareness of physiological signals of tension, use of adequate coping, or prevention of aggressive behavior. Ryan mentioned that the Sense-IT app signaled tension when there was none and did not signal tension when there was; questioning the accuracy of the feedback.This participant, aged between 30 and 35, had severe aggression regulation problems. At baseline (T0), Ryan achieved a high score (9th decile) on the AQ-SF compared to other outpatients with violent behavior. He reported predominantly reactive aggression on the RPQ. Furthermore, Ryan experienced many emotion regulation difficulties (DERS). His anger expression index on the STAXI-2 (95th percentile) shows that he tended to express his emotions more outward than inward, and that his ability to regulate his emotions was very low. Using the piecewise regression results and visual analysis see , his fee1), while aggressive thoughts seem to decrease. The increase in aggressive behavior reached statistical significance. In the intervention phase (B), these variables all seem to change in decreasing direction. None of these decreases reached statistical significance. Mean HR seems stable. When the patterns in aggressive behavior were compared between both phases, his outcomes did significantly favor the intervention phase. No significant differences between phases were found on other variables. Compared to baseline, Eric also achieved a lower score on the AQ-SF (4th decile) at post-test. His score on the DERS remained the same. Eric reported no effect of biocueing on his awareness of physiological signals of tension, use of adequate coping, or prevention of aggressive behavior. He stated that he was not inclined to act upon the physiological feedback he received.This participant, aged between 40 and 45, also struggled with aggression regulation problems. At baseline, he scored average on the AQ-SF compared to the norm group (6th decile). Eric also reported predominantly reactive aggression on the RPQ. He experienced emotion regulation difficulties (DERS) to an average degree. The anger expression index (81st percentile) indicates that Eric also tended to direct his anger more outward than inside, and that his regulation skills were quite low. The piecewise regression results and visual analysis see  illustra1), which might favor the intervention phase. Compared to baseline, Joshua scored slightly lower on the AQ-SF (5th decile) at post-test. The score on the DERS also decreased slightly, but was still in the same range. Joshua did notice a positive effect of biocueing on his awareness of physiological signals of tension. He explained that the app helped him not to get stuck in anger by using adequate coping strategies, such as seeking distraction and clearing his mind. He did not notice an effect on prevention of aggressive behavior as he felt he had not been aggressive during the research period.This participant, aged between 20 and 25, also had aggression regulation problems. At baseline, Joshua scored average compared to other outpatients with violent behavior (6th decile). He reported similar levels of reactive and proactive aggression on the RPQ. Joshua experienced emotion regulation problems (DERS) to an average degree. His anger expression index (60th percentile) indicated that he expressed his anger in both directions, and more inward compared to the other Ryan and Eric. His regulation skills were average to good. The piecewise regression results and visual analysis see  revealed3.4.“I felt the tension building and when it [the watch] started to vibrate I recognized that I was angry and went to do something else” (P16) and another reported: “That my heart rate is often high even though I do not think so myself.” (P3) However, other patients indicated: “I was often alerted to tension when I already knew it.” (P21) and “The app sends false notifications: it vibrates while nothing is going on and not when you are stressed out” (P1). Approximately half of the participants (48%) felt supported by the Sense-IT app to use coping strategies in order to reduce stress. For example, one participant reported: “When the watch vibrated I took the notification into account, for example by coming to myself and taking a breath.” (P17) and another said: “I went to do something else and cleared my mind instead of dwelling in anger.” (P10). In some cases, the coping strategies used may have shed light on less adequate behavioral patterns:“[When the watch vibrated] I went to smoke a joint and got calmer. I also went gaming.” (P9). Another participant mentioned: “I will not be guided by a watch. Did look at the notification, but did not react to it. Screw it, I’ll just stay angry.”(P8), indicating that at least some motivation to change behavior and openness to feedback are necessary prerequisites to benefit from the app. Moreover, it shows that it can be difficult for some patients to distinguish anger from aggression. Finally, about one-third of the participants (38%) reported that using the app might have helped them to prevent aggressive behavior in some cases. One participant responded: “Maybe. By participating in this study, I became more aware to reduce my anger when the watch indicated, for example when stressed at work.” (P25). Other participants mentioned that they were able to calm themselves in some cases, but not in all: “Sometimes I took it easy, but sometimes I was just angry and did not do anything.” (P9) or: “Sometimes you do not think about the watch, then things go fast and something happens.” (P7). Another participant stressed the boundaries of an app: “It takes more than that. When I am very aggressive, I will not be stopped by a vibrating watch or a mobile with questions” (P16). Furthermore, several participants reported increased stress and irritation by the number of notifications and the perceived inaccuracy of the app: “Actually, it increased my frustration and irritation. I had to actively suppress not throwing the thing away.”(P21). Other participants indicated that this last question was difficult to answer as they had not experienced aggressive outbursts in the past period.Qualitatively, we focused on the perspectives of patients regarding the perceived effectivity of the Sense-IT app on interoceptive awareness, use of coping strategies, and prevention of aggressive behavior. The responses of the participants can be found in 4.In the current study, we explored the effects of a new version of the Sense-IT biocueing app on interoceptive awareness, emotion regulation, and aggressive behavior in a forensic outpatient population. In this study, the Sense-IT app was added to regular ART. Quantitatively, we examined group changes on measures of aggression, emotion regulation and insight in anger bodily sensations between pretest, posttest, and follow-up (pretest-posttest design), as well as group and individual changes in behavioral control, aggressive behavior, anger, aggressive thoughts, physiological tension and HR in the intervention phase compared to the baseline phase (SCEDs). In addition, we qualitatively assessed patient-reported changes in interoceptive awareness, use of coping strategies, and prevention of aggressive incidents associated with the use of the Sense-IT app.The results of the pretest-posttest design showed a significant decrease in self-reported aggression between pretest and posttest, indicating a positive effect associated with the combination of the Sense-IT biocueing intervention and ART. In addition, although on visual inspection emotion regulation difficulties decreased in this sample, no significant effects were found. Furthermore, no significant differences were found for therapist-reported aggression level and insight into anger bodily sensations. The quasi-experimental nature of this design prohibits attribution of the found effect to either the combined intervention or the regular therapy alone. However, the fact that the significant decrease in aggression was only found between pretest and posttest (the period the biocueing was added) and not for the comparison with follow-up (the period regular therapy was continued without the biocueing intervention), is interesting, and could be further investigated in future studies using a controlled research design.Due to low compliance to EMA and a low amount of sufficient HR measures per day part, data analysis of the SCEDs was limited to EMA data of 9 participants and HR data of 10 participants. Focusing on the primary measures, aggressive behavior and behavioral control, an interaction effect favoring the biocueing intervention was found for one participant (a decrease in aggressive behavior in the intervention phase compared to an increase in the baseline phase). Regarding exploratory measures, a reverse pattern was found in another participant (an increase in aggressive thoughts in the intervention phase compared to a decrease in the baseline phase). For another participant, mean HR decreased in the intervention phase compared to an increase in the baseline phase. On group level, we found no significant developmental effects in the baseline and intervention phase, no significant immediate changes when transitioning into the intervention phase, and no significant interaction effects on any of the variables. Overall, effect sizes were small, with some individual exceptions to moderate.In contrast, the qualitative results do indicate positive changes related to the use of the Sense-IT biocueing app, such as increased interoceptive awareness among the majority of the participants, perceived support in the use of coping strategies by half of the participants, and prevention of some aggressive incidents by one-third. Although most participants reported increased insight into physiological signals of tension, results show that the step from insight toward adequate emotion regulation requires more attention. Furthermore, results seem to indicate that a certain amount of motivation to change behavior and openness to feedback are necessary to benefit from the just-in-time behavioral support delivered by the Sense-IT app. Although the usability of the Sense-IT app was acceptable , some paIn sum, out of the subgroup of patients who qualitatively reported positive changes associated with the use of the biocueing intervention, we only found a significant quantitative change favoring the intervention phase for one patient with sufficient data for single-case analysis. We therefore conclude that, whereas the quantitative results of the pretest-posttest design and the qualitative results indicated positive changes associated with the combination of biocueing intervention and ART, the repeated ambulatory measurements of the SCEDs do not indicate a clear effect favoring the addition of a biocueing intervention.First of all, we would like to shed some light on the findings related to interoceptive awareness. The awareness of bodily sensations has been identified as an important component in the process of emotional awareness , which iSecond, we would like to focus on the findings regarding emotion regulation. Although half of the participants qualitatively reported that they felt supported by the app to use coping strategies in order to reduce stress, and on visual inspection emotion regulation difficulties decreased in this sample, no significant effects associated with the combination of biocueing intervention and ART were found in the pretest-posttest design. We also found no group or individual increases in behavioral control favoring the biocueing intervention in the SCEDs. Several factors might have contributed to these findings. First, motivation to change and feedback receptivity varied among the participants. Although these factors might not be required to take advantage of the component of the Sense-IT app aimed at increasing interoceptive awareness, feedback receptivity turned out to be quite essential to benefit from the behavioral support component of the Sense-IT app, even among non-psychiatric samples without motivational difficulties . For futThird, we would like to reflect on the findings regarding aggressive behavior. First of all, as patients reported in the qualitative part of the study, it is hard to determine if  aggressive incidents had been prevented and if so, whether that could be associated with the use of the biocueing intervention. Regarding aggression, the pretest-posttest results did indicate a significant decrease in aggression associated with the combination of biocueing intervention and ART. A significant decline in aggressive behavior favoring the biocueing intervention was found in one participant in the SCEDs However, no group effects were found using the repeated ambulatory measurements of the SCEDs, and findings were not supported by therapist-reported aggression level. Several factors are worth noting. First, the EMA responses showed a low prevalence of aggressive behavior in most patients. This created a bottom effect in some patients. Whether this is related to social desirability, lack of concept clarity, or an actual low incidence rate remains unclear. Second, the added value of therapist-reported aggression levels should be considered limited as these scores were not only based on actually observed aggressive incidents but also on patients’ reports thereof during the weekly therapy sessions. As some social desirability might have been at play in both therapist- and patient-reported aggression , patient4.1.Several strengths and limitations could be applied to this study. A noteworthy strength is that this study is one of the first in which a biocueing intervention, using psychophysiological measures, is used as an addition to regular treatment in a complex forensic outpatient sample with anger regulation difficulties. As main end-users, the forensic outpatients were involved throughout the developmental process, delivering us with valuable feedback and recommendations for future use of the app. Another strength is the use of a mixed-methods design. Integrating quantitative and qualitative data, on group and individual level, enabled us to explore the effects of the combination of a biocueing intervention and ART and to extract relevant information for further development and implementation in clinical practice.2). Therefore, only the results of the baseline phase (A1) and intervention phase (B) could be analyzed, for a select group of patients. Fortunately, most patients, including those who prematurely stopped using the app, still participated in the post-measurements of the pretest-posttest design. Although we thereby reduced the risk of bias in the quantitative and qualitative group results, some patients with negative experiences did still just return their devices and reported that they did not want to participate anymore. Another limitation related to the SCEDs is the fact that we were unable to (randomly) assign participants to different lengths of the baseline phase. Since we had already difficulty engaging participants, we had to let go of specific days that would match the research design but would not fit in the schedule of the participants. Since we only found small effects the impact of the missing multiple baseline analysis seems negligible. Fourth, limitations in the usability of the Sense-IT app may have overshadowed the effects of this additive intervention. Connectivity issues, other design preferences, restricted ability to customize the settings, use of a research-owned smartphone, and limited battery life of the smartwatch are disadvantages that are extensively discussed in another study , as suggested in experience sampling literature . Despiteer study . For nower study , it wouler study . Fifth, 4.2.Since it is known that a lot of end-users stop using a mental health app if their goals and preferences are not met , it is i4.3.In sum, the qualitative results indicate that the use of a biocueing intervention as an addition to regular ART could be considered a helpful means to increase interoceptive awareness among forensic outpatients. Furthermore, during the combination of this new intervention and regular ART significant decreases in self-reported aggressive behavior were observed. However, results of the repeated ambulatory measurements of the SCEDs do not indicate a clear effect favoring the addition of a biocueing intervention. On group level, no significant effects were found. On the individual level, effects favoring the intervention condition were only found for two participants. Decreasing compliance to the demanding research design, the possible therapeutic effects of the daily EMA questions, and limitations in both usability and integration in therapy, might have impacted the results and hampered interpretability. Future research and development should focus on increasing usability, tailoring the intervention to individual needs, and on integration into therapy. Furthermore, research should further investigate the individual characteristics  associated with effective support by the Sense-IT app, as the use of personalized treatment interventions in clinical practice, including new technological interventions, is only expected to increase in the coming years.The datasets presented in this article are not readily available because of participants privacy. Requests to access the datasets should be directed to the corresponding author.The studies involving human participants were reviewed and approved by METC, VUmc: NL63911.029.17. The patients/participants provided their written informed consent to participate in this study.JH, MN, AP, and TP conceived the entire study and developed the study designs. JH and LS performed the qualitative analysis. JH coordinated the data collection and processing, wrote the manuscript, and analyzed the quantitative data in close cooperation with MN, who provided an expert assessment and contributed R code. MN, LS, TP, AG, and AP critically revised the manuscript. All authors read and approved the final manuscript.This study was performed within a larger research project, financially funded by the Dutch Ministry of Justice and Security (no grant code available).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


To investigate the risk factors for deep vein thrombosis (DVT) in children with osteomyelitis and provide diagnostic and treatment strategies for the prevention, early detection and treatment of DVT.The clinical data of nine children diagnosed with osteomyelitis and DVT between July 2012 and March 2021 were collected at our hospital, including age, sex, clinical manifestations, body temperature, coagulation function and other data, as well as the clinical data of 27 children diagnosed with osteomyelitis without DVT during the same period. Thirty-six children were divided into thrombus and thrombus-free groups. The clinical characteristics and risk factors for DVT in children with osteomyelitis were analysed.Staphylococcus aureus (MRSA) was associated with DVT. MRSA was the independent risk factor for DVT.Among the 36 children in this study, nine cases of thrombus formation mainly occurred in the femoral vein, popliteal vein and iliac vein, all near the infection site. The main clinical manifestations were lower extremity pain, swelling and pulmonary embolism in three cases. Among them, intensive care unit (ICU) admission, sepsis, higher D-dimer, higher body temperature during hospitalization, and pathogen culture showed that methicillin-resistant Admission to ICU, sepsis, higher D-dimer, higher body temperature during hospitalization, and MRSA are risk factors for thrombosis. MRSA is the independent risk factor for DVT. For patients with related risk factors, timely ultrasound examination of the infected site should be considered to achieve early detection and treatment. The occurrence of thrombosis in the paediatric population is progressively rising. Among these, deep vein thrombosis (DVT) is found to be the most prevalent, presenting an incidence rate between 0.07 and 0.49 per 10,000 . DVT, chData on children with DVT, who were diagnosed with colour ultrasound, were collected from those admitted to our hospital between July 2012 and March 2021. Children diagnosed with osteomyelitis were screened, their medical records were queried to obtain clinical data, and they were selected as the thrombus group. Children with osteomyelitis who were hospitalized simultaneously without thrombosis were selected as the thrombus-free group at a ratio of one:three. The inclusion criteria for the thrombus group were as follows: ① thrombosis confirmed by ultrasound, with clinical manifestations and test results consistent with osteomyelitis; and ② age from zero to eighteen years. The exclusion criteria for the thrombus group were as follows: ① thrombus related to catheterization and ② incomplete clinical data. The inclusion criteria for the thrombus-free group were: ① hospitalized children diagnosed with osteomyelitis during the same period, whereas the exclusion criterion was incomplete clinical data. Finally, nine and 27 children with and without thrombi, respectively, were included in this study. General data , clinical symptoms, thrombus site, coagulation function and other clinical data of the two groups of children were compared and analysed to determine the clinical characteristics and risk factors for thrombus formation in children with osteomyelitis. This study was approved by the Ethics Committee (2022 LUN Review [Research] No. 175), and all participants’ guardians provided informed consent. The study was checked by strobe checklist [U-test was used for comparison. Categorical data were presented as frequencies, and the Chi-squared test or Fisher’s exact test was used for comparison. Binary logistic regression analysis was performed for the statistically significant factors in the univariate analysis, presenting odds ratios (ORs) and their 95% confidence intervals (CIs). A p value of less than .05 was considered statistically significant.SPSS26.0  was used for statistical analysis. As the data were not normally distributed, measurement data were expressed as median and interquartile range (Q1–Q3), and the Mann–Whitney Blood counts, C-reactive protein (CRP) measurements and sedimentation measurements were performed on admission; Doppler ultrasonography was performed in children with localized, clinically significant osteomyelitis. Bacteriology included blood cultures on admission and sampling of the infected site for bacterial culture at the time of surgery.Among the nine children with thrombosis, seven were males, accounting for 77.8% (seven out of nine), and two were females, accounting for 22.2% (two out of nine), with a male-to-female ratio of 3.5:1. The age range was 27–160 months, with an average age of 96.6 months and a median age of 125.0 months. The largest proportion of children were in the 10–13 age group, with five out of the nine children falling into this category, representing 55.6% of the total.The main sites of thrombosis in the nine children were as follows: femoral vein in eight out of nine cases (88.9%), popliteal vein in six out of nine cases (66.7%), iliac vein in four out of nine cases (44.4%), posterior tibial vein in two out of nine cases (22.2%) and anterior tibial vein in one out of nine cases (11.1%). The locations of the thrombi corresponded to the infection site, and there were seven cases of multi-site thrombosis .Among the nine children with thrombus, there were clinical symptoms at the site of thrombus, which were caused by the coincidence of the location of the primary disease, osteomyelitis and thrombus. The main clinical manifestations were lower limb swelling, pain and joint pain. At the same time, PE (chest pain and dyspnoea) occurred in three cases .Staphylococcus aureus (MRSA), with sepsis and maximum body temperature. Univariate analysis showed that children with no surgical treatment, higher body temperature, higher D-dimer level, ICU admission and MRSA were more likely to develop thrombosis than those without thrombosis  admission, body temperature, white blood cell (WBC), CRP, erythrocyte sedimentation rate (ESR) and D-dimer level were collected in both groups. The culture strain analysed was methicillin-resistant rombosis . Factorsrombosis .The epidemiology, anatomical location and clinical presentation of DVT differ between children and adults. The incidence of DVT in children is much lower than that in adults, and in most cases occur in hospitalized children as serious complications of other diseases and their treatment . ReporteStaphylococcus aureus infection is the leading cause of osteomyelitis in most paediatric hospitals. MRSA is an extremely common pathogen in hospitals and communities. Every year, paediatric hospitals experience a steady increase in the rate of methicillin-resistant staphylococcal infections [Osteomyelitis is reported to affect between 1 and 25 children per 100,000 children each year, with an incidence peak in early childhood, that is more common in boys than in girls. Acute blood-borne osteomyelitis is more common in children than in adults, and severe cases require treatment . Currentfections . In addiDVT is relatively rare in children. In children diagnosed with osteomyelitis, lower extremity swelling and pain can be observed during physical examination, and osteomyelitis combined with DVT can be confirmed by performing lower extremity colour ultrasound. The age groups with a high incidence of DVT in children are infants 20%) and adolescents (50%), which was consistent with the age characteristics of the nine cases in our study. Although the total incidence was low, it showed an increasing trend annually . The mec% and adoIn this study, there was little difference in the mean age between patients with or without DVT . Hollmig et al.  reportedIn this study, the sex of children with DVT was mostly male , which was not significantly different from that of children without thrombosis . This predominance of males has also been reported in literature; all patients reported by Gorenstein et al.  and eighThe primary limitation of this study is its retrospective design; children with osteomyelitis were not monitored or followed up. Another limitation is the lack of prospective clinical or radiological studies to detect DVT, as most are incidental findings on infection-related imaging. Few patients present with the clinical symptoms of DVT, except for pain and swelling of the limbs caused by osteomyelitis . TherefoInformed consent for publication of photographs was obtained from all study participants. The Ethics Committee of the Children’s Hospital of Chongqing Medical University approved the study.


The expertise of surgeons and surgery performed by high-volume surgeons were associated with fewer complications and lower cost. We aimed to describe and compare the number of surgeries, mortality rate, and length of hospital stay for adrenalectomies performed between 2008 and 2019 in the public health system of São Paulo.  this was an ecological study. The data were collected using the TabNet Platform of the Unified Health System Department of Informatics. Outcomes analyzed included the number of surgeries performed, mortality rate during hospital stay, and length of hospital stay. Public hospitals in Sao Paulo were divided into three subgroups according to the surgical volume of adrenalectomies performed as well as hospitals with and without a residency program in Urology, and the results were compared among them.  a total of 943 adrenalectomies were performed in Sao Paulo between 2008 and 2019. Mortality rates during hospital stay according to hospital surgical volume were no reported deaths in low-volume, 0.015% in intermediate-volume, and 0.004% in high-volume hospitals. The average length of the ICU stay was 1.03 days in low-volume, 2.8 in intermediate-volume, and 1.12 in high-volume hospitals .  despite no statistically significant differences among the groups analyzed, mortality rates were very low in all groups. ICU stay was shorter in high-volume centers than in intermediate-volume centers. Adrenal tumors are usually asymptomatic and, thus, are frequently diagnosed through abdominal imaging done for unrelated reasons. Incidentalomas are found in up to 9% of autopsy cases and in 4% of imaging seriesHormonal assessment is mandatory after diagnosis to prove or exclude overproduction of cortisol, aldosterone, or catecholamines to differentiate functioning from non-functioning masses. Literature shows that complication rates - glucose intolerance, cardiovascular disease, and dyslipidemia - are comparable between patients with functional and non-functional adrenal tumorsHormonal production by an adrenal mass has a genetic cause, but it is not entirely clearFew adrenal tumors are malignant. The majority of pheochromocytomas (PCC) are benign, but up to 25% may be malignant, which is a rare neuroendocrine malignancy arising from neural crest adrenal medulla cells. It is estimated with an annual incidence of 0.8 per 100,000 people, and approximately 500-1,600 new cases per year in the United States. PCC is highly vascular and secretes catecholamine, presenting with the over-activation of the sympathetic nervous systemAdrenocortical carcinomas have a lower incidence, with a more aggressive presentation and high mortality rate,Treating benign  and malignant adrenal cancer includes adrenalectomy. The presence of symptoms or biochemically functioning tumors is an indication for surgical intervention in adrenal masses. If asymptomatic, the size (<4cm) and imaging characteristics (<10UH) may be indications for observation managementAdrenal carcinoma is more likely in larger lesions, with tumors larger than 6cm having a 19% chance of malignancy, reaching 47% in lesions larger than 8cmThe prevalence of secondary arterial hypertension varies from 3% to 5%. Parenchymal kidney disease and sleep apnea are the most common causes of secondary hypertension. Other causes include renovascular, primary hyperaldosteronism, pheochromocytoma, aortic coarctation, Cushing’s syndrome, hypothyroidism, hyperthyroidism, hyperparathyroidism, and acromegalyPrimary hyperaldosteronism is a disease characterized by inappropriate aldosterone secretion and plasma renin suppression. The most common causes of primary hyperaldosteronism are aldosterone-producing adenomas and bilateral adrenal hyperplasia, but they can also be caused by adrenal carcinoma and extra-adrenal tumors that produce aldosterone. Conventionally, the prevalence of primary hyperaldosteronism is not highly uncommon, and hyperaldosteronism is present in about 20% of the population of resistant hypertensive patientsIt is interesting to note that the meta-analysis by Bancos et al. demonstrated benefits after adrenalectomy in the control of cardiovascular risk factors in patients with adrenal tumors when compared to conservative treatment,,The access used to perform an adrenalectomy can be: transabdominally, retroperitoneally, or transthoracic. The technique commonly used is transabdominal adrenalectomy by open or minimally invasive surgery (MIS)To minimize the intraoperative complications of an adrenalectomy, we must perform extensive exposure and visualization of the operative field and isolating the main vascular structures and resection using the block technique to prevent capsular ruptureth largest population worldwide, and it is estimated that in 2016, it had approximately 12 million inhabitants and almost 5 million of them relied on SUS.The expertise of surgeons and surgery performed by high-volume surgeons were associated with fewer complications and lower costOn the other hand, the role of radiation and chemotherapy is limited, and the effect of adjuvant mitotane is unprovenThe objective of this study was to describe and compare the number of surgeries, mortality rate during hospital stay, length of hospital stay, ICU stay, and costs of adrenalectomies performed by SUS between 2008 and 2019 in São Paulo city according to hospital surgical volume and the difference between hospitals with and without residency programs.This is an ecological study that analyzed data available from the TabNet platform of the Unified Health System Department of Informatics (DATASUS), which provides open data on procedures performed through the Brazilian public health care system. Data are unidentified on the platform; informed consent was not feasible and therefore not requested by the local ethics committee. The study was approved by the Ethics Committee at Hospital Israelita Albert Einstein (number CAAE: 17208019.0.0000.0071).We examined three procedural codes of the Unified Health System Table of Procedures, Medications, Orthoses, Prostheses, and Materials Management System (SIGTAP/SUS) for the treatment of adrenal diseases: “bilateral adrenalectomy” - code 0402020014, “unilateral adrenalectomy” - code 0402020022 and “adrenalectomy in oncology - code 0416010202” All information was retrieved from publicly available websites using web scraping software. Coding was performed in Python v. 2.7.13  running on a Windows 10 Single Language operating system . Data collection, field selection, and table sorting were performed using open-source packages selenium-webdriver v. 3.1.8  and pandas v. 2.7.13 . The web scraping code had a main structure with 14 adaptive search steps (Appendix A) for the different filters available on the platform. We used Mozilla Firefox v. 59.0.2 browser  and GeckoDriver v. 0.18.0 webdriver . ® v. 16.0.4456.1003 spreadsheet (Microsoft Corporation). The following information was extracted from the TabNet dataset: total number of adrenalectomies performed, mortality rate during hospital stay, and length of hospital stay and costs. Following data collection, standard procedures of data transformation and cleaning were performed for each file, including the removal of header and footer information, removal of the health facility code, and conversion of date columns to rows. The data were saved and stored in Microsoft Office Excel 2016The public hospitals of São Paulo were divided into three subgroups according to the volume of surgeries performed (tercil), and the results were compared among themselves. Nine centers were included as low volume, performing 1 to 3 surgeries. Another nine hospitals classified in the intermediate group performed between 4 and 14 surgeries. Finally, eight centers were included as high volume and performed between 29 and 470 surgeries. Hospitals were also divided into two groups: with and without a residency program in Urology and the results were compared as well.Statistical analysis was performed using SPSS v. 24 . Quantitative variables were analyzed with the Mann-Whitney U test or T test according to the Kolmogorov-Smirnov test for normality. Proportions were analyzed using the chi-square test. Statistical significance was determined at p<0.05.A total of 943 adrenalectomies were performed in São Paulo city between 2008 and 2019 in 26 hospitals in São Paulo. The number of surgeries performed increased over the years, especially after 2012. The lowest number of surgeries was in 2008, with 44 procedures and the highest in 2018, with 113 .Global mortality during hospital stay was very low, with 5 deaths among 943 surgeries (0.005%). The mortality rate divided by hospital volumetry is shown in The average length of hospital stay per surgery was 5.47 days in low volume, 13 days in intermediate volume, and 6.35 days in high-volume hospitals, with statistical significance when comparing low with intermediate volume centers (p=0.041) . ConsideThe lowest volume center was related to less cost compared to higher volume centers  .Comparing institutions with residency programs in Urology and without residency programs in Urology, length of hospital stay, ICU hospitalization, mortality, and costs did not show significant differences between both groups .The adrenal gland produces hormones fundamental to the functioning of the organism with the production of aldosterone, cortisol, and androgenic hormones being in the cortical, noradrenaline, and adrenaline being in the marrow. The uncontrolled production of these hormones triggers clinical complications with a high rate of morbidity and mortality when not properly treated. Our public health system has shown an increase in the number of patients undergoing adrenalectomy in recent years, with greater access by the population to modern diagnostic tests. However, there is still an underreporting of patients who could benefit from the treatment of adrenal tumors.Another aspect shown in our series is the prolonged period of hospitalization of operated patients, probably related to the lack of knowledge of the true nature of pathologies related to the adrenal gland and the lack of coordination between clinical, surgical, and anesthesia teams, due to the metabolic changes that these patients present, requiring multidisciplinary treatment.This is the first Brazilian study based on information extracted from a public health system database of a low-to-middle-income country that compared the outcomes of adrenalectomies according to hospital surgical volume. All data analyzed in our study are publicly available and were collected from the Unified Health System Department of Informatics (DATASUS), created in 1991 to provide SUS with information systems and computer support services. The Unified Health System Table of Procedures, Medications, Orthoses, Prostheses, and Materials Management System (SIGTAP/SUS) provides information on which SUS-affiliated institutes are qualified to perform each procedure and help improve financial decision-making.When we analyzed adrenal diseases in our city, we did not identify differences between hospitals with and without a residency program when we compared the following aspects: length of hospital stay, ICU hospitalization, intrahospital mortality, and costs. Perhaps this can be explained by the study by Park et al., who identified that the main factor related to complications is the volume of surgeries performed by the surgeon. In hospitals with medical residency, we have residents with little surgical experience performing adrenalectomies guided by experienced surgeons while in hospitals without medical residency, surgeons with high surgical baggage  may also be present, which can match the results,The authors indicated the operative time and intraoperative complications reduction accruing the experience, leading to locating the flattening of the learning curve after approximately 30 cases were performed. Eto et al. reported a shorter learning curve for surgeons after participating in at least 10 procedures as an assistant surgeonAnother explanation for the lack of difference between teaching and non-teaching hospitals is that usually the most complex cases or those involving malignant neoplasms are referred to university centers, while simpler cases are often performed in smaller hospitals. This study presents a bias for not stratifying the reason for the adrenalectomy performed by each service.It was observed that adrenal surgeries performed by higher volume centers are associated with fewer unfavorable clinical outcomes compared to intermediary volume centers. These observations are in agreement with previously published findings by other investigators. According to Al-Qurayshi Z et al., the centralization and selective referral of patients who require adrenalectomy to high-volume surgeons holds conceivable savings potential for patients overallSeveral studies support the importance of referral centers in accomplishing the learning curve. Although in recent years, thanks to the improvement of diagnostics, the number of adrenal pathologies requiring surgery has greatly increased, they still have a low incidence, and adrenal surgery is still rarely performed in non-referral centersOther studies have worked with thousands of peopleNonetheless, Kazaure and SosaCurrently, 9% of hypertension cases are attributed to secondary endocrine causes. In fact, hypertension is curable surgically in a minority of patients, especially in patients with aldosterone-secreting adenomas or with functional pheochromocytomas or paragangliomasThis study has some limitations, such as data source, which was restricted to the SUS database, depending on the notification of the procedures and outcomes by each institution. In the database system, we had access to limited outcomes. The DATASUS depends on the proper filling of data by health professionals, in addition this database does not cover primary care, only secondary and tertiary care, so some important information about patients’ demographics, comorbidities, and symptoms or tumor type and location, and anatomopathological exams are not provided. The number of adrenal surgeries has increased over the years. Despite no statistically significant differences among the groups analyzed, mortality rates were very low in all groups. ICU stay was shorter in high-volume centers than in intermediate-volume centers. Costs were lower in low-volume centers.