Individual health literacy, attitudes, and competencies towards HPS and occupational self-efficacy served as independent variables and the level of HPS implementation was the dependent variable. Data were analyzed using univariate and bivariate analysis as well as multiple binary logistic regression. Results: 29.3% of school leaders show a limited health literacy with significantly higher values found for male respondents. Regression analyses revealed that male gender , HPS attitudes , and HPS competencies  were associated with a lower level of HPS implementation. Furthermore, regression analyses differentiated by gender showed that limited health literacy is associated with a low level of HPS implementation for male school leaders only . Conclusions: The promotion of health literacy especially for male leaders would not only result in positive effects on an individual level but also could contribute to a stronger implementation of activities on school health promotion. We suggest integrating health literacy, HPS attitudes, and competencies more strongly into the qualification and in further training of school leaders.Background: The promotion of health literacy is seen as an urgent goal in public health and education and, hence, should be integrated in the school context as a component of the holistic health promoting school (HPS) approach. However, only limited empirical studies have addressed health literacy of school staff so far. Hence, this study aimed to examine the level of health literacy among school leaders and its association with the extent of HPS implementation. Methods: A cross-sectional study with  Worldwide, schools as learning and teaching environments are recognized as appropriate venues for health promotion and prevention that reach a large proportion of young people, teachers, and nonteaching staff . In addin = 42 physical education teachers shows that the support of the school principal proved to be a significant predictor for the implementation of the Comprehensive School Physical Activity Program (CSPAP), i.e., the higher the principals’ support, the more components of the CSPAP were implemented [n = 1.140 school principals and n = 1.110 HPS coordinators  indicate that school leaders’ understanding of the HPS and their willingness to sustain HPS at their school were significantly associated with higher levels of HPS implementation and teachers’ willingness to implement HPS on a continuing basis [Compared to highly standardized  intervention programs, that are delivered with high implementation fidelity, the HPS approach is characterized by context sensitivity that requires adaptations to the specific conditions and dynamic processes of a single school ,14,15. Dlemented . Next tolemented  reportedlemented ,21. Moreng basis . With ren = 719) revealed that men were more likely to show a limited functional health literacy  [n = 585 Korean adults aged 19 years and older, Lee et al. [In addition to the abovementioned school leader-oriented influencing factors, the importance of general health-related competencies has been increasingly discussed in public health research in recent years. Today, the research field offers a plethora of health literacy definitions and models, covering a wide array of concepts from narrow functional health literacy through comprehensive, generic health literacy to disease-specific health literacy and topic-related health literacy ,24. Desp.16–3.5) . Moreovee et al.  reportede et al. . Researce et al. , and hige et al. . In viewe et al. ), it is e et al. ,36,37,38n = 502 secondary school teachers from Sri Lanka revealed a limited health literacy for 31.5% of the respondents with higher percentages found for younger teachers (≤45 years) and those with less teaching experiences (≤10 years) [In contrast to a separate intervention strategy, it is argued that the promotion of health literacy should be seen as a component of the holistic HPS approach and should be developed from within the schools and the educational system ,38,39. T0 years) . Moreove0 years) . Besides0 years) . Similar0 years) .However, the few studies available so far relate to the professional group of teachers while no findings are available on the health literacy of school leaders. This is surprising given the fact that this group is of high importance for the entire school and the quality of its teaching and learning conditions, processes, and results ,46. MoreAgainst this background, the present study aims to explore the health literacy of school leaders and its association with the level of HPS implementation. In most studies mentioned above, school leaders have been regarded as a homogeneous group, i.e., subgroup differences within this occupational group are rarely employed. Since primary schools are more oriented towards the holistic development of the child, it would be reasonable to assume that primary school leaders support activities on school health promotion more strongly, resulting in higher HPS implementation. In this context, the results of a German study showed that primary school leaders indicated the highest and that secondary school leaders indicated the lowest need for support in the area of school health promotion . MoreoveWhat is the level of health literacy among school leaders?What sociodemographic and work-related characteristics are associated with health literacy of school leaders?What is the relationship between health literacy of school leaders and the level of HPS implementation taking into account sociodemographic characteristics and other school leader-oriented factors ?Specifically, the following research questions are addressed:n = 2.039), the study was also implemented in the federal states of Berlin (n = 237), Lower Saxony (n = 1.336), and Schleswig-Holstein (n = 714) from 2014 to 2016 in cooperation with the respective school leadership associations. To ensure comparability, a core questionnaire was used in each study, which was supplemented by additional scales and items.This cross-sectional study was part of a larger survey which aimed to investigate the work and health situation of German school leaders and to examine their role in the implementation of school health promotion. The study was carried out for the first time as an online survey in the German federal state North Rhine-Westphalia from late 2012 to early 2013. Due to the high response rate  in general and vocational schools. The study protocol was approved by the Hesse Ministry of Education. All eligible participants were invited by email and newsletter, including two reminders by the Hesse association of school principals. The survey was administered electronically using the Enterprise Feedback Suite (EFS) survey tool by Questback. Participation was voluntary, and anonymity was assured. Upon entering the survey site, participants were presented with information regarding the aims and the background of the study. After checking a consent box at the bottom of the page, participants were directed to the questionnaire.demographic and work characteristics include gender , age , type of school , schools for children with special educational needs , vocational school, others , and professional role .General Health literacy as an independent variable was assessed using the German short version of the European Health Literacy Survey Questionnaire (HLS-EU-16) [r = 0.82) [S-EU-16) ,50. The  = 0.82) ,50. The  = 0.82) ,50. No s = 0.82) ,51. For personal attitudes towards HPS were measured with six items [HPS related competencies were operationalized with three items adapted from Wilde et al. [occupational self-efficacy scale by Abele et al. [Based on the existing evidence on the association of school principals on HPS implementation, we included attitudes, competencies, and self-efficacy as independent variables. Adapted from a study on workplace health promotion, ix items . These ie et al.  that cape et al. . In conte et al. , and in Level of HPS implementation was operationalized using an 8-item scale by Harazd et al. [n = 6). Based on their feedback, slight changes in the formulations were made. Two example items are “Health promotion and health goals are integrated in the mission statement and program of our school.” and “At our school, pupils are supported in the development of health-promoting behaviour.” The internal consistency (Cronbach’s α) for the overall scale was 0.90. For further regression analyses, this indicator has been dichotomized using median-split (1 = low level of HPS implementation and 0 = high level of HPS implementation).The implementation level of the HPS served as dependent variable in this study. d et al. , which cχ2). Moreover, Pearson correlation (two-tailed) for all independent and dependent variables were conducted. Finally, multiple binary logistic regression analyses were used to examine the association of all explanatory variables  with the level of HPS implementation by odds ratio (OR) and its respective 95% confidence interval (95% CI). This form of regression analysis was chosen because there are no predefined cut-off values for the dependent variable resulting in an empirical  division of low versus high HPS implementation (using median-split). All independent variables were included block-wise. Block 1 covered sociodemographic and work characteristics, while block 2 (in addition to the variables of block 1) included HPS attitudes, competencies, and self-efficacy. Block 3 included health literacy in addition to the variables of the previous blocks. The estimated fit of the regression models was provided by Nagelkerke’s R squared. Again, chi square tests were used for testing the significance of the different models. For all analyses p-values < 0.05 were considered statistically significant.All statistical analyses were performed using IBM SPSS version 23 . First, we analysed the data descriptively . Second, all potential sociodemographic and work characteristics were cross tabulated with the two levels of health literacy (limited vs. sufficient) and their associations were assessed using chi square tests , between the age of 46 to 60 years (60.7%), and had the role of the main school principal (81.2%). Moreover, almost half of respondents were from primary schools (48.5%), another 10% worked at a grammar school, and about 9% worked at a school for children with special educational needs  reported a sufficient level of health literacy, while 23.5% had a problematic and 5.8% an inadequate health literacy. χ2(1) = 4.87, p < 0.05). While no differences in health literacy could be identified between school principals and members of the management board , descriptive results showed a higher percentage of having a limited health literacy for middle aged respondents  and respondents from vocational schools (31.4%). However, differences among age groups and type of school did not prove to be significant.With regard to gender, chi square tests revealed a significant difference with a higher percentage of limited health literacy found for male compared to female leaders , standard deviations (SD), and Pearson correlations of all explanatory variables and the dependent variable is shown in r = 0.11, p < 0.001), HPS competencies , and occupational self-efficacy . Furthermore, level of HPS implementation as dependent variable correlated on a moderate level with HPS attitudes  and HPS competencies , while small associations with occupational self-efficacy  could be found.A summary of the means  was explained by the explanatory variables of the full model (block 3).All variables from bivariate (correlation) analyses with significant associations . When health literacy was entered in block 3, significant associations between limited health literacy and low-level HPS implementation could only observed for male school leaders . Nagelkerke’s R2 of 0.30  and 0.29  indicate that about one third of the variation between the two groups (low and high HPS implementation status) was explained by the explanatory variables of the full model (block 3).For block 2, a heterogeneous picture emerged for male and female respondents. Although HPS attitudes and competencies were significantly linked with the dependent variable for both genders, the size of the odds ratios differed in the gender comparison. While male school leaders with low HPS attitudes showed a higher increase of the probability of a low level of HPS implementation , the opposite could be observed with regard to HPS competencies with higher odds ratios found for female leaders . However, two-tailed difference tests of the odds ratios revealed no significant gender differences (HPS attitudes: n = 2.000) in the German Health Literacy Survey (HLS-GER) had a limited health literacy [n = 4.854) [The present study aimed to examine, firstly, the level of health literacy of school leaders and its connection with the implementation status of health promotion in schools. Our results revealed that 29.3% of the school principals and administrators had a limited level of health literacy. Compared with the results of representative German surveys, the percentage of respondents with limited health literacy is substantially lower. For example, 54.3% of the respondents  . Differe= 4.854) , researc= 4.854) .Secondly, there is a particular need for support especially for male school leaders, who are more often affected by limited health literacy than female respondents (36.5% vs. 26.9%). It is argued that these gender differences can be traced back to the traditional division of gender roles, which ascribes greater responsibility to women for the health of family members  ,57,58. IThird, besides initial findings on the level of health literacy, the main results of our study indicate that health literacy is linked to the implementation status of the HPS even after controlling for sociodemographic and work characteristics as well as further school-leader oriented influencing factors. School leaders with sufficient health literacy levels were more likely to report a higher level of HPS implementation. They may be able to apply their individual-level abilities  to an organisational context to support a variety of activities on school health promotion. Thus, our findings somewhat contradict the results of the study by Lai et al. , in whicWith regard to the school-leader-oriented influencing factors, our results confirm the findings of previous studies ,20,21,22When interpreting the results of our study, several limitations need to be considered. First, the sample is not representative and the results cannot be generalised to the whole population of school leaders in the federal state of Hesse. According to the school statistics for the 2017/18 school year, there were 1804 schools in Hessen, of which more than half (57%) are primary schools. A further 10% of all schools from Hesse was schools for children with special educational needs, while 7% was grammar/high schools and 6% was vocational schools. Due to missing public statistics, no comparisons can be made between the study sample and the general population of school principals and administrators from the federal state of Hesse. Moreover, it should also be taken into account that, due to the group of eligible participants addressed , more than one person per school were able to participate in the survey. This could have led to a positive bias, as, e.g., leaders from well-implemented HPS felt more strongly addressed and motivated to participate. Second, based on the cross-sectional design of this study, the results concerning the association between health literacy and level of HPS implementation cannot be interpreted as causal relationships. This is of particular importance as there is no empirical clarity about the multifaceted relationship between health literacy and health promotion so far . BesidesThis study adds to the existing research in various ways. To our knowledge, this is the first study examining the health literacy of school principals and school managers. In their overall responsibility for the entire school, school principals serve as gatekeepers for any school  development process and, hence, need to be put more firmly in the focus of school health promotion research. Although school leaders belong to a group with a high level of education, our results indicate that slightly less than a third of the respondents are affected by a limited health literacy and, hence, should be supported in strengthening their individual abilities to search for and to deal with health-related information, including their use and healthy decision making. On the school level, activities should focus on implementing a culture for health literacy embedded within the HPS approach, whereas on the individual level, activities should focus specifically on male school leaders, who more often have a limited health literacy compared to female respondents. Moreover, it could be shown for the first time that individual health literacy is associated with holistic practices on school health promotion, thus placing individual health literacy more in the context of a socio-ecological understanding of health. Gender differences found in this study underline the need for gender-specific actions to strengthen individual health literacy. Next to the promotion of health literacy, these activities should also encompass strategies to strengthen attitudes and competencies towards HPS. These could be achieved by integrating these issues more strongly into the qualification phase and in further training of school leaders. Finally, further research is needed to confirm the study results presented here  and to examine causal relationship between health literacy and organisational practices on school health promotion.


This study was performed to propose a method, the Feature Ambiguity Mitigate Operator (FAMO) model, to mitigate feature ambiguity in bone fracture detection on radiographs of various body parts. A total of 9040 radiographic studies were extracted. These images were classified into several body part types including 1651 hand, 1302 wrist, 406 elbow, 696 shoulder, 1580 pelvic, 948 knee, 1180 ankle, and 1277 foot images. Instance segmentation was annotated by radiologists. The ResNext-101+FPN was employed as the baseline network structure and the FAMO model for processing. The proposed FAMO model and other ablative models were tested on a test set of 20% total radiographs in a balanced body part distribution. To the per-fracture extent, an AP (average precision) analysis was performed. For per-image and per-case, the sensitivity, specificity, and AUC (area under the receiver operating characteristic curve) were analyzed. At the per-fracture level, the controlled experiment set the baseline AP to 76.8% , and the major experiment using FAMO as a preprocessor improved the AP to 77.4% . At the per-image level, the sensitivity, specificity, and AUC were 61.9% , 91.5% , and 74.9% , respectively, for the controlled experiment, and 64.5% , 92.9% , and 77.5% , respectively, for the experiment with FAMO. At the per-case level, the sensitivity, specificity, and AUC were 74.9% , 91.7%% , and 85.7% , respectively, for the controlled experiment, and 77.5% , 93.4% , and 86.5% , respectively, for the experiment with FAMO. In conclusion, in bone fracture detection, FAMO is an effective preprocessor to enhance model performance by mitigating feature ambiguity in the network. X-ray examination is the primary and initial measure in radiographic examinations for fracture patients, and fractures can be diagnosed with certainty in most cases in this way. Although X-ray radiographs only provide limited information due to limited projection views and clinic observations, they still yield crucial evidence for triage determination. Accuracy and instantaneity of the report on the radiograph is necessary to provide patients with proper treatment. With the rapid development of artificial intelligence (AI), researchers have begun to dig in employing deep learning AI to improve both the speed and accuracy of X-ray diagnoses of fractures. Most researchers use classification or object detection pipelines of the AI technique to detect fractures in radiographs. To do this, most of the authors actuate deep learning models to work in a binary classification task, i.e., classifying the radiographs into two groups of “fracture” and “non-fracture”. However, this method usually applies a large quantity of data for analysis. Olczak et al.3 extracted 256,458 hand, wrist, and ankle radiographs and marked them with classification labels. In their dataset, the VGG16 network achieved an 83% accuracy. The limitation of this study was that the images had to be resized to 256 × 256 resolution , which could make small fractures undetectable. This problem was fixed by Gale et al.4 by processing the image through cropping and resizing a region around the neck of the femur (area of interest) to 1024 × 1024 pixels. Huang et al.5used a densely-connected convolutional neural network (CNN) structure, the DenseNet, and achieved an AUC (area under the curve) of 99.4%, which indicates that with appropriate architecture and massive amounts of data on specific body parts, deep learning models can be extremely precise. Despite the astonishing performance, the model was limited to the frontal view, neglecting those fractures hidden in other views. Moreover, large amounts of data have always been required, and hence the tremendous cost of time. Kitamura et al.6 attempted to minimize the training set data while maintaining the ability of the detecting system. In their study, they used only 1441 of frontal and lateral ankle images as the training data, employed the de novo training and ensemble technique, and achieved an accuracy of 81%, which was comparable to the Olczak’s 83% accuracy achieved by using a massive data3.Orthopedic trauma or fracture is an important part of clinical epidemics7 proposed an object detection pipeline, the Faster-RCNN, to locate the region of fracture on wrist radiographs by “bounding-box”. Trained on around 6780 images, the method hit an AUC of 91.8% in the frontal view and 93.3% in the lateral view on each image level. Lindsey et al.8 tried another way, the semantic segmentation, to locate fractures in the target wrist area (ulna and radius) by identifying whether each pixel in the image is fracture pixel or not. In their study, the average sensitivity was 80.8% with an 87.5% specificity on the 135,845 body parts labeled, including 34,990 anteroposterior and lateral wrist views based on the 31,490 images used as the final segmentation (detection) for training.Lack of interpretability has always been a denounced aspect of the deep CNN learning methods. Classification pipelines can only judge “fracture” or “non-fracture” in the entire image, thus being less useful. Realizing this, Thian et al.9 and FPN10 were updated to set a strong baseline. These measures were to decrease the amount of data used to analyze fractures to about 1000 frontal and lateral X-ray radiographs, and by using far fewer data, our purpose was to test if a good diagnostic performance could be achieved.In previous studies with the AI technique to help diagnose bone fractures, a large quantity of data is needed, and lack of interpretability and imaging ambiguity are also two shortcomings. To overcome these shortcomings, we proposed the Feature Ambiguity Mitigate Operator (FAMO) method to mitigate the feature ambiguity of bone fracture images in this study. Moreover, an object detection pipeline was employed, and two network structures of ResNeXtWe conducted experiments to verify that the proposed FAMO operator can improve the fracture detection performance.As shown in Tables The per-image and per-study ROC curve were illustrated in Figs. We performed hypothesis tests for both per-image and per-case level, which hypothesized that the FAMO performance was worse than the performance in the control group model. The P-values to deny them were shown in the Table In different body-parts Figs.  and 4, tIn this study, we presented the FAMO plus ResNeXt101+FPN model to mitigate the feature ambiguity of bone fracture images using far few data to analyze the radiograph images and had reached a diagnostic performance as good as that by radiologists, but better than that by the ResNeXt101+FPN model only. By using 406 to 1651 desensitized radiographs of different body parts, the FAMO plus ResNeXt101+FPN model achieved better sensitivity, specificity, and AUC than the ResNeXt101+FPN model only, and the FAMO plus ResNeXt101+FPN model also obtained a per-case AUC mostly above 80% in different body parts.3 extracted 256,458 wrist, hand, and ankle radiographs and achieved an accuracy of at least 90% when identifying laterality, body part, and exam view in all networks with the final accuracy for fractures being estimated at 83% for the best performing network. This network performed similarly to senior orthopedic surgeons when presented with images at the same resolution as the network. In the study using CNN for automated fracture detection and localization on wrist radiographs, Thian et al.7 used around 6780 images to reach an AUC of 91.8% in the frontal view and 93.3% in the lateral view on each image level. In the study of deep neural network for improving fracture detection, Lindsey et al.8 used the semantic segmentation to locate fractures in the target wrist area (ulna and radius) by identifying whether each pixel in the image is fracture pixel or not. In this study, they used 135,409 radiographs to reach a sensitivity of 80.8% (95% CI: 76.7–84.1%) unaided and 91.5% (95% CI: 89.3–92.9%) aided, and a specificity of 87.5% (95% CI: 85.3–89.5%) unaided and 93.9% (95% CI: 92.9–94.9%) aided. The misinterpretation rate was averagely decreased by 47.0% (95% CI: 37.4–53.9%).In the study of AI for analyzing orthopedic trauma radiographs, Olczak et al.The FAMO plus ResNeXt101+FPN model proposed in our study achieved better sensitivity, specificity, and AUC with the per-case AUC mostly above 80% in different body parts. However, there are also some limitations in our study. First, the data were imbalance to some extent, and the amounts of some body parts were far from sufficient for a deep learning system. Second, radiologists had annotated segmentation of fracture areas, which we did not make use of. The minor experiments revealed that a model only supervised by box label rather than segmentation label could have better performance, the cause of which was not investigated in this study. Third, the data for training and testing were from the same institution with no involvement of multiple centers, and the robustness of the model on the data of different distributions from different centers remained to be verified. Future studies will have to resolve these issues for better performance.In conclusion, the novel operator FAMO proposed in this study is able to mitigate the feature ambiguity in X-ray bone fracture detection, improving both sensitivity and specificity across per-fracture, per-image, and per-case level in all body parts.This study was approved by the institutional review board of the Third Hospital of Hebei Medical University (K2019-036-1), and the study was conducted in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Written informed consent was waived due to the retrospective nature of this study. All methods were performed in accordance with the relevant guidelines and regulations. A total of 9040 desensitized radiographs of various body parts and projection views were drawn from the pictures archiving and communication system (PACs) and annotated separately for training, validating and testing. The dataset distribution is shown in Table 8. The data labeling was improved in three folds. Firstly, considering bone blocks in perspective X-ray images sometimes stacking over each other, the doctors were required to label an instance segmentation instead of semantic segmentation, which allows areas of different fractures to overlap. Secondly, parts that should be included in a fracture segmentation area were precisely defined, including fracture surface, articular surface collapse, and abnormal bone trabecula. Clarifying fracture symptoms not only eliminated the heterogeneity of labels marked by different doctors, but also helped them stay alert as they kept focusing on delineating fracture areas. Thirdly, all labels were finally carefully reviewed by the chief physician with 20 years of experience. The bounding-boxes used to train and test the models were generated automatically according to each instance segmentation, a process that could harvest more accurate bounding-boxes than direct annotations. The main purpose of this study was to compare the effect of the FAMO model and baseline model, so data labeling by the physician was used as the gold standard. The consistency (credibility) of data labeling by physicians is shown in Table The data were labeled by two radiologists with ten-year’s experience using the segmentation method similar to that by Lindsey et al.11 to equalize the luminance on the same region among different patient images. Images were resized to the scale so that long side length was close to 800 pixels while maintaining the aspect ratio. Augmentations were employed during training, including scale and aspect ratio floating, image shifting, pixel noise, brightness floating, 360-degree random rotation, random flip, and random color inverse.Image arrays in 16-bit integer format were extracted from Digital Imaging and Communications in Medicine (DICOM) files. The raw data were processed with adaptive histogram equalization12 operator according to the global box predictions, and the whole image was cropped simultaneously by the same boxes to make corresponding local labels. Local features were forwarded by Region-based Convolution Neural Network (RCNN)-Head decoder to fit local box labels. Finally, at the clinic scene, RCNN-Head decoder would pick fracture lesion from radiographs instance-by-instance.The artificial neural network learning model was built on the ResNeXt+FPN architecture, which was the state of the art in the object domain Fig. . Pre-pro10 encoder which is an enhanced version of a typical ResNet encoder was used. The ResNext was made by stacking several identical ResBlocks which are a serialized connection of the convolution, group convolution, non-linear activation function, batch normalization layer, and down-sampling layer. Because each ResBlock ends with a down-sampling layer, the final feature map output will be down-sampled by 4 × 2 n times, where n represents the number of ResBlocks stacked. The output of ResNext has a low definition, such that we cannot localize fracture lesions precisely if using the output feature directly. As such, the FPN (Feature Pyramid Network) was employed to handle this problem. The FPN extracts output feature maps from intermediate ResBlock, concatenates lower definition features with higher definition features by up-sampling the lower one, and adds them together element-wise. Such fusion approach gathers the high level global and the low level local semantic information to harvest the accurate instance prediction. Also, the output of FPN is multi-scale, which makes the network sensible to the objects of different sizes. There is a Region-Proposal decoder supervised and trained by bounding box labels which enclose the rectangle area of fracture lesion; this decoder inferences a Region of Interest (RoI) for each pixel on the FPN output feature maps. The RoI-Align operator then crops on the feature maps according to the RoIs and resizes them into a fixed size, and the RoI features with the highest score predicted by region proposal decoder (RPN Head) would be fed to the next classification and regression decoder (RCNN Head). The classification decoder aims to judge the box prediction to fit the lesion enough or not while the segmentation decoder is designed to refine box prediction.The whole structure is essentially an encoder-decoder structure but connected to a so called RoI-Align operator. The input of the network is one radiograph and its label. The radiograph was fed into the CNN encoder, and the ResNext+FPNIn object detection pipelines, the RoI-Align operator is the bridge connecting the global feature and local fracture feature. It is supposed to crop regions proposed by the RPN on the global feature and resize them to unitive square local feature. This operation should be on the assumption that shape deforming would not influence classification. However, in fracture detection, it causes feature ambiguity, which is demonstrated in Fig. Fractures can be in various directions Fig. . When thThe operator adjusts the annotated box by expanding its short side to the same length as its long side and making a square box  optimizer of linear warming-up learning rate from 0.001 to 0.005 through 500 iterations. According to the linear scaling rule7, where the per-image true positive level requires one box true positive, and the per-case true positive requires one box true positive on any one projection view. For the per-fracture level, the specificity is not testable, because “negative boxes” prevailing in the image which caused specificity for the per-fracture level were always greater than 99%. So instead of using specificity, the true positive was treated as numerator and the sum of true positive and false positive as the denominator to measure in what portion that model predictions were actually fracture.The model was evaluated in three levels: per-case, per-image, and per-fracture level. For per-image level and per-case level, the same metric was used as that used by Thian et al.The Average Precision (AP) metric is widely used in object detection research. It averages the precisions at different recall levels, ranging from 0.0 to 1.0. To harvest various precision-recall pairs, different thresholds were set to the box score output by the model, where lower threshold harvested higher recall and higher threshold harvested lower recall.The statistical analysis was performed with the SPSS 20.0 . The sensitivity, specificity, and AUC in the receiver operator characteristics curve was analyzed. The significant P was set at < 0.05.


Scapulohumeral periarthritis (SP) is a very common painful shoulder disorder. Several systematic reviews (SRs) and meta-analyses have reported the effectiveness of acupuncture for patients with SP. However, the evidence has not been systematically synthesized. This overview aims to map, synthesize, and assess the reliability of evidence generated from these SRs and meta-analyses of acupuncture for SP.We will electronically search the following databases for literature, regardless of publication status and language: the Cochrane Central Register of Controlled Trials (CENTRAL); PubMed; EMBASE; China National Knowledge Infrastructure (CNKI); Chinese Biomedical Literature Database (CBM); Chinese Scientific Journal Database (VIPdatabase); and Wan-Fang Database. In order to ensure the comprehensiveness and accuracy of the literature retrieval, we will combine the Suggestions of evidence-based medicine experts with the actual situation in the literature retrieval process to formulate the retrieval strategy, and make corresponding records to find the most appropriate retrieval strategy. The reference lists and the citation lists of studies meeting the inclusion criteria and relevant SRs will also be searched to identify further studies for inclusion. Before this review completed, the two reviewers will conduct the searching once again to ensure the latest studies could be included.Ethical approval is not required for overviews. We plan to publish results in peer-reviewed journals and present at international and national academic, clinical, and patient conferences.The results will be published in a peer-reviewed journal.This overview will provide comprehensive evidence of acupuncture for patients with SP.INPLASY202060020. Shoulder pain and limited mobility affect people in every way, prompting treatment for this disease is essential.,7Scapulohumeral periarthritis (SP), as known as frozen shoulder or adhesive periarthritis of shoulder, is a common musculoskeletal disorder in middle-aged people. It commonly displays shoulder ache and shoulder dyskinesia as characteristic caused by intraarticular and extraarticular adhesion gradually, which is a chronic inflammation and degenerative disease located in soft tissue, such as shoulder muscles, tendons, ligaments, and capsule.,8 Numerous studies have demonstrated the efficacy of intra-articular (IA) steroid injection in pain reduction, shoulder function, and range of motion (ROM) improvement in the short term. Moreover, these treatment options may cause severe adverse events, such as infectious arthritis and cartilage damage.,10The treatments of SP are mainly conservative, including intra-articular triamcinolone injection and bupivacaine suprascapular nerve blocks. acupuncture is an extensively accepted alternative therapy, as an important part of complementary and complementary medicine, has gained increased popularity for the management of constipation around the world. acupuncture has the functions of enhancing immunity, regulating blood circulation, and preventing diseases. Some clinical trials have found that acupuncture has a significant impact on SP. In addition, it has the advantages of safe, reliable, and is easy to use without toxic and side effects. However, the evidence has not been systematically synthesized. This overview aims to map, synthesize, and assess the reliability of evidence generated from these systematic reviews (SRs) and meta-analyses of acupuncture for SP. Thus, this study will investigate the effect and safety of acupuncture and moxibustion for patients with SP.Acupuncture can relieve pain, improve the blood circulation, stimulate metabolism of local tissue, etc1.2 It is believed that acupuncture works by releasing endogenous opioids in the body that relieve pain, by overriding pain signals in the nerves, or by allowing energy (qi) or blood to flow freely through the body. Some studies have shown that acupuncture therapy of acute periarthritis of scapulohumeral suggests filiform acupuncture, distal acupoint selection and strong stimulation of purging. Among them, two schemes of “Tiaokou point penetrating Chengshan Point” and “local adjacent point cooperating with Tiaokou point” are recommended for needle acupuncture.,17Acupuncture is an ancient treatment method based on traditional Chinese medicine (TCM), which has existed in China for about 2500 years. It mainly uses acupuncture to stimulate specific acupoints or surface parts of the body to stimulate the related functions of acupoints, and stimulates the meridians and vital energy to regulate the zang-fu organs, so as to treat diseases. Nowadays, the indications for moxibustion are gradually expanded because of its function and practicality, The heat generated by moxibustion can effectively play the role of replenishing Yang and dispelling cold, promoting blood circulation and removing blood stasis, and warm the meridians and collaterals. It has been found in modern studies that the warm effect and light radiation produced by moxibustion are one of the most important factors in the effective treatment of SP. The heat of combustion can promote blood circulation, relax blood vessels, and regulate the body's immune system. The far infrared ray produced by moxibustion can directly act on the superficial part of the human body and diffuse the heat by conduction. The near infrared ray energy is strong, can penetrate into the deep tissue directly, provides the necessary energy for the body cell activity.Moxibustion has a significant effect on analgesia of SP, with less trauma, less risk, and less adverse reactions.1.3The objectives of this study are as follows:1. and Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA)Explore the methodological and reporting quality of available SRs using the Assessment of Multiple systematic Reviews-2 (AMSTAR-2)2.Summarize the best current evidence for the effectiveness and safety of acupuncture and moxibustion for SP through qualitative analysis of the outcomes.3.Provide more reliable, evidence-based medical references for clinical practitioners and researchers22.1 This protocol was recorded in the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY), registration number INPLASY202060020 (https://inplasy.com/inplasy-2020-6-0020/). And if there are any changes, we will describe it in our full review.It is an overview protocol that follows the recommendations of the Cochrane Handbook for Systematic Reviews of Interventions.2.2Population, Intervention, Comparison, Outcome and Study (PICOS) strategy was employed.2.2.1For this overview, only SRs and meta-analysis of randomized controlled trials (RCTs) for moxibustion in people with Scapulohumeral periarthritis, published in English and Chinese, will be included. All relevant SRs and meta-analyses of randomized clinical trials published in English and Chinese about acupuncture for SP will be included.2.2.2Study participants of different age ranges with all types of SP will be included, regardless of sex, race, occupation, education, nationality, etiology, and severity.2.2.3Acupuncture  and moxibustion  will be included as a single intervention or major part of a combination therapy with other active interventions .2.2.4The comparative interventions will be sham acupuncture, sham moxibustion, placebo, no treatment, or other active treatments. The following treatment comparisons will be performed:1.Acupuncture and moxibustion vs no treatment2.Acupuncture and moxibustion vs placebo or sham acupuncture or sham moxibustion3.Acupuncture and moxibustion vs other active therapies4.Acupuncture and moxibustion + active therapy vs the same active therapy2.2.52.2.5.1Main outcome indicators: pain relief visual analog scale (VAS) score, ROM, Melle Score of shoulder joint functional activity or other validated scales, at least after one week of treatment.2.2.5.2Additional outcomes include the following aspects:1.Secondary outcome indicators: total effective rate;2.Safety outcome: adverse reactions.2.2.62.3We will electronically search the following databases for literature, regardless of publication status and language: the Cochrane Central Register of Controlled Trials (CENTRAL); PubMed; EMBASE; China National Knowledge Infrastructure (CNKI); Chinese Biomedical Literature Database (CBM); Chinese Scientific Journal Database (VIP database); and Wan-Fang Database. In order to ensure the comprehensiveness and accuracy of the literature retrieval, we will combine the Suggestions of evidence-based medicine experts with the actual situation in the literature retrieval process to formulate the retrieval strategy, and make corresponding records to find the most appropriate retrieval strategy. The reference lists and the citation lists of studies meeting the inclusion criteria and relevant SRs will also be searched to identify further studies for inclusion. Before this review completed, the two reviewers will conduct the searching once again to ensure the latest studies could be included.The search will be restricted to human subjects, while there is no restriction on any specific languages. The proposed search strategy for PubMed is presented in Table 2.4Studies will be identified using NoteExpress 3.0. After the initial removal of duplicate studies, two reviewers (ZNW and YXY) will independently screen titles and abstracts based on the eligibility criteria. Full-text studies will be retrieved for all potentially includable SRs or SR protocols. If studies contain insufficient information to make a decision about eligibility, QSX will try to contact authors of the original reports to obtain further details. During the procedure, disagreements will be resolved by discussion or consensus with the third reviewer (GXW). Study selection will be performed in accordance with the PRISMA flowchart Fig. .2.5Two researchers (ZNW and YXY) extracted the literature information according to the inclusion and exclusion criteria, including the following aspects:1.Study characteristics: author, year, study design, sample size and follow-up time;2.Patient characteristics: age, sex, and type of SP;3.Intervention: intervention measures in the experimental group, intervention measures in the control group);4.Outcome of the study: Two researchers (GXW and QSX) cross-examined the results of extraction of the included literature. In case of differences, a third party (JX) should be consulted to resolve them.2.6For each SRs that meets the inclusion criteria, two reviewers (ZNW and YXY) will use the Assessment of Multiple systematic Reviews-2 (AMSTAR-2) Measurement Tool and PRISMA to assess methodological quality and report quality. At the same time, differences arising during the evaluation process will be discussed by two reviewers and arbitrated by a third author (JX).1.AMSTAR-2 consists of 16 items, 7 of which are critical areas. The 16 criteria are rated as “yes” (clearly completed), “no” (clearly not completed), “cannot report” (unclear completion), or “not applicable” based on the information provided by the reviewer to the system review when the criteria are met.2.PRISMA: This is a 27-item list. Each checklist item will be evaluated as yes, no, or partially Yes to indicate compliance.2.7 which will then be submitted to other two authors (GXW and ZYZ) for approval. We will use the GRADE Grid instrument to review each recommendation one by one to group it into one of five options, including “strong recommendation,” “weak recommendation,” “unclear recommendation,” “weak no recommendation,” and “strong no recommendation.” The aim is to reach a better consensus. If 75% of the experts agree on an option, there is consensus on the recommendation. Otherwise, the project goes to the next Delphi process to discuss the disputed project again.After the evidence is evaluated by the Grading of Recommendations Assessment, Development and Evaluation (GRADE), the quality of the evidence, the weights of strengths and weaknesses, and the patient's values and preferences will be carefully considered by two authors (ZNW and YXY) to develop preliminary recommendations. This proposal will be reviewed by 2 to 3 rounds of Delphi process,2.8When we find insufficient or unspecified data in the published SRs, it will be necessary to contact the author by phone or email with the necessary information. If the data we need cannot be collected completely, it will be considered useless and abandoned. After that, we will analyze the existing data and discuss the potential impact of missing data2.9Two of our researchers (QSX and ZNW) will use the bias risk tool provided by the Cochrane Collaboration to evaluate the quality of the literature using RevMan 5.3 software. This recommended tool includes 7 important items: sequence generation, allocation concealment, blinding of participants and personnel, blinding of results evaluation, incomplete result data, selective result reporting, and other biases. Make “Low risk,” “High risk,” and “unclear risk” judgments for each research literature. Finally, a “risk of deviation” summary and a chart are generated to show the results. As with the previous process, it will be independently assessed by 2 researchers. If there is disagreement, it will be discussed with the 3rd researcher (JX).2 test. When statistical homogeneity was found among the results , a fixed effect model was used for meta-analysis of the results. If statistical heterogeneity exists among the results , the source of heterogeneity needs to be analyzed. If statistical heterogeneity exists between the two results and the difference is not statistically significant, a random-effects model should be used for Meta analysis. When there is too much heterogeneity among the results of various studies, statistical methods such as subgroup analysis, sensitivity analysis, and descriptive analysis can be used to treat the heterogeneity.RevMan 5.3.3 software was used for Meta analysis of the research objects. Counting data were represented by relative risk (RR) and its 95%CI, and measurement data were represented by mean difference (MD) and its 95%CI. Heterogeneity among all included studies was detected by χ3We have developed this study based on the principles and standards of evidence-based medicine, in collaboration with multidisciplinary experts, which will facilitate the treatment of SP by clinicians, as well as for teaching and educating patients.Based on what we know so far, we have found some limitations of this Protocol:1.Most of the sites included in the study are in China;2.The application of acupuncture and moxibustion in other countries and regions needs further study.3.At the same time, the differences of acupuncture and moxibustion operation methods between different countries should also be need considered.This study has a number of contributions:1.to the best of our knowledge, this is the first protocol to assess acupuncture and moxibustion therapy for patients with SP;2.the results of this protocol will be beneficial to acupuncturists and physicians to make decisions the optimal method of treating the disease, and help patients with SP seeking optimal treatment;3.the results are helpful to find out the correct operation method of acupuncture and moxibustion for treating SP and the relationship between the therapeutic effect, the time of acupuncture and moxibustion and the total amount of acupuncture and moxibustion, effectively improving the efficacy and safety of SP with acupuncture and moxibustion.Conceptualization: GuoXin Wu, Jun Xiong, ZhengYun Zuo.Data curation: GuoXin Wu, XingYu Yu, ZeNanWu.Formal analysis: XingYu Yu, QingShan Xie.Investigation: Jun Xiong, GuoXin Wu. Methodology: Jun Xiong, XingYu Yu, ZeNan Wu.Software: GuoXin Wu, QingShan Xie.Supervision: Jun Xiong, ZhengYun Zuo.Writing – original draft: Jun Xiong, GuoXin Wu, XingYu Yu, ZeNan Wu.


At the same time, parental education is one of the main drivers of reward responsiveness among adolescents. It is still unknown if some of this racial and economic gap is attributed to weaker effects of parental education for African Americans, a pattern also called minorities’ diminished returns (MDRs). (2) Aim: We compared non-Hispanic White and African American adolescents for the effects of parent education on adolescents RR, a psychological and cognitive construct that is closely associated with high-risk behaviors such as the use of drugs, alcohol, and tobacco. (3) Methods: This was a cross-sectional analysis that included 7072 adolescents from the adolescent brain cognitive development (ABCD) study. The independent variable was parent education. The main outcome as adolescents’ RR measured by the behavioral inhibition system (BIS) and behavioral activation system (BAS) measure. (4) Results: In the overall sample, high parent education was associated with lower levels of RR. In the overall sample, we found a statistically significant interaction between race and parent education on adolescents’ RR. The observed statistical interaction term suggested that high parent education is associated with a weaker effect on RR for African American than non-Hispanic White adolescents. In race-stratified models, high parent education was only associated with lower RR for non-Hispanic White but not African American adolescents. (5) Conclusion: Parent education reduces RR for non-Hispanic White but not African American adolescents. To minimize the racial gap in brain development and risk-taking behaviors, we need to address societal barriers that diminish the returns of parent education and resources in African American families. We need public and social policies that target structural and societal barriers, such as the unequal distribution of opportunities and resources. To meet such an aim, we need to reduce the negative effects of social stratification, segregation, racism, and discrimination in the daily lives of African American parents and families. Through an approach like this, African American families and parents can effectively mobilize their resources and utilize their human capital to secure the best possible tangible outcomes for their adolescents. Reward responsiveness (RR) , a traitBased on Gray’s reinforcement sensitivity theory (RST) , RR is oRelative to their non-Hispanic White counterparts, African American adolescents are at an increased risk of high-risk behaviors. For example, African American adolescents are more likely than non-Hispanic White adolescents to be at risk of aggression , early sGiven the close overlap between race and parental education in the US , researcThere are at least two complementary theories that provide an explanation for how race and parental education jointly impact adolescents’ outcomes. The first theory, dominant in the literature, and more commonly used as an explanation of the inequalities, attributes the observed racial gap in adolescents’ outcomes to the observed differences in parental education and other family SES indicators across racial groups ,48,49,50Minorities’ diminished returns (MDRs) ,57, the As described above, the MDR literature suggests that the educational attainment of oneself  and one’To extend the science on what we already know about the role of RR as a mechanism for explaining MDRs for high-risk behaviors such as aggression, tobacco use, and impulsivity, in this study, we explored the combined effects of race and parent education on adolescents’ RR. Thus, we compared African American and non-Hispanic White adolescents for the effect of parent education, a strong family SES determinant of adolescents’ various behaviors, and RR. We expected a weaker effect of parent education on RR for African American than non-Hispanic White adolescents.We performed a secondary analysis of data from the adolescent brain cognitive development (ABCD) study ,83,84,85Participants of the ABCD study were adolescents ages 9–10 years old. Adolescents in the ABCD study were recruited from multiple cities across states. Overall, there were 21 sites that recruited adolescents to the ABCD study. The recruitment of the ABCD sample was mainly done through school systems. A detailed description of the ABCD sampling is available here . Four thThe study variables included race, demographic factors, parent education, parental marital status, and RR.Reward responsiveness (RR). In this study, RR was measured using the behavioral approach system (BAS)  developeRace was self-identified. Race was a categorical variable and coded 1 for African Americans and 0 for non-Hispanic Whites (reference category). All people of a Hispanic background were excluded. We used the one drop rule to handle people who identified as both White and African American. That means individuals would be considered African American if they identify as both African American and White.Participants were asked, “What is the highest grade or level of school you have completed or the highest degree you have received?”. Responses were 0 = never attended/kindergarten only; 1 = 1st grade; 2 = 2nd grade; 3 = 3rd grade; 4 = 4th grade 4; 5 = 5th grade; 6 = 6th grade 6; 7 = 7th grade 7; 8 = 8th grade; 9 = 9th grade; 10 = 10th grade 10; 11 = 11th grade; 12 = 12th grade; 13 = high school graduate; 14 = GED or equivalent diploma; 15 = some college; 16 = associate degree: occupational; 17 = associate degree: academic program; 18 = Bachelor’s degree (ex. BA); 19 = Master’s degree (ex. MA); 20 = professional school degree (ex. MD); and 21 = Doctoral degree. This variable was coded in two distinct ways. First, it was coded as measured. This was an interval measure with a range between 1 and 21. Second, we adopted the Jaeger  coding aAge, sex, parental marital status, and household size were the confounders. Parents reported the age of their adolescents. Age (years) was calculated as the difference between the date of birth to the date of the enrollment to the study. Sex was a dichotomous variable: males = 1, females = 0. Parental marital status was a dichotomous variable. This variable was self-reported by the parent who was interviewed. This variable was coded as married = 1 vs. other = 0. Household size, reported by the parent, was a continuous measure.t test to test bivariate associations between race and study variables. For our multivariable modeling, we performed four linear regression models. Our first two models were performed in the overall sample. Model 3 was performed without the interaction terms. Model 4 added an interaction term between race and parental education attainment. Then we performed two additional models specifically to race (race-stratified models). Model 3 was tested in non-Hispanic White and Model 4 was tested in African American adolescents. Our models used age, sex, marital status, and household size as the covariates. We ran identical models using various coding of educational variable. Our first model used the census and our second variable used the Jaeger [t value, and p value were reported for each model. p value equal or less 0.05 were statistically significant.We used the statistical package SPSS to perform our data analysis. Mean (standard deviation (SD)) and frequency (%) were described depending on the variable type. We also performed a Chi square and independent sample e Jaeger  code of The ABCD study received an Institutional Review Board (IRB) approval from the University of California, San Diego (UCSD). Each adolescent provided assent. Each parent signed an informed consent . As thisn = 5099; 72.1%) and the rest were African Americans . As shown in Model 1 (the main effect model) showed a protective effect of high parent education against RR. Model 2 (the interaction model) showed a statistically significant interaction between race and parent education on RR, which was suggestive of a weaker protective effect of high parent education on RR for African American adolescents relative to non-Hispanic White adolescents.Model 3 (non-Hispanic Whites) showed protective effects of high parent education on RR of non-Hispanic White adolescents. Model 4 (African Americans) did not show any protective effects of high parent education on RR for African American adolescents.In this study, while high parent education was associated with lower RR overall, this was only true for non-Hispanic White but not African American adolescents, as shown by the pooled sample as well as by race-stratified models. This suggests racial minority status limits the boosting effect of parent education on RR for American adolescents.The observed diminished returns of parental education on RR is very similar to the previous publications on the MDRs of parental education and income on impulsivity , ADHD 7, and inhMDRs are not specific to a specific resource or age group, outcome, or even marginalizing identities ,57. EducVarious sociological, economic, and behavioral mechanisms are involved in explaining the MDRs of parent education for African American families. African American parents and families experience high levels of economic, social, general, and race-related stress in their daily lives at all SES levels . Racial Residential segregation results in differences in African American and non-Hispanic White environmental and contextual exposures. Due to segregation, school options are different for high SES African American and Hispanic families. As a result, children of high SES African American families attend highly segregated schools with low resources ,71,111. While lower SES of African Americans is one type of disadvantage, MDRs reflect another class of disadvantage ,57. WhilMultilevel economic, psychological, and societal mechanisms may be involved in explaining racial gaps in the returns of parental education ,113. MDRAfrican American families are more likely to stay in poor neighborhoods despite high SES. Highly educated African Americans are more likely to stay poor than non-Hispanic Whites ,118. SimAs this study showed, health disparities are not all due to SES differences, and some can be seen across all SES spectrum. This means, it is not race or SES but race and SES that shape health disparities ,125,126.While this study’s main association of interest was the effect of parental education on RR, we also found auxiliary results. We found results considering gender. In the pooled sample, boys showed a higher RR than girls. In Whites but not Blacks, males had higher RR. A higher reward responsiveness of males than females is known. This is also related to the higher impulsivity ,130,131,We also want to make it clear that MDRs are not due to Kelly’s Paradox  or regreThis study, like any other studies, comes with a specific set of methodological limitations. As our data were cross-sectional in nature, we could not draw any causal inference between race, parent education, and adolescents’ RR. Similarly, we only tested the MDRs of parent education. Future research should test if MDRs go beyond parent education and hold for other SES indicators such as income, wealth, class, occupational prestige, and neighborhood SES indicators. Finally, this study only described the MDRs of parent education on RR and did not seek to understand the contextual factors that cause such MDRs.In this study, RR  was measRelative to their non-Hispanic White counterparts, African American adolescents show lower parent education and higher RR. This adversity in African American youth is compounded by another profound and systemic disadvantage, weaker effects of parent education on adolescents RR. As a result of the latter disadvantage, African American adolescents show low RR across all parental education levels. That means some of the racial inequalities in RR remains across all educational levels. In other terms, racial inequalities in RR show a spill-over effect in middle-class people. As a result of high RR, African American adolescents engage in a high risk of behaviors across all levels of parental education. This is in contrast to the pattern for non-Hispanic White adolescents who show a social patterning of their RR. That is, for non-Hispanic White youth, RR is lowest when parental education is highest.


The mechanisms underlying the relative age effect (RAE), a biased distribution of birth dates, in sport events have been investigated for more than two decades. The present study comprised an historical analysis involving the most recent quarter-century (1993–2018) on RAEs among Japanese male professional athletes  to clarify how the RAEs changed over time.Birth data were obtained from 7805 Japanese male professional athletes registered in 1993, 2001, 2010, and 2018. The athletes were divided into four groups based on their month of birth: quartiles Q1 (April–June), Q2 (July–September), Q3 (October–December), and Q4 (January–March of the following year). In addition, based on the data in 1993 for soccer and baseball and in 2010 for basketball and volleyball, the expected numbers of players were calculated in 2001, 2010, and 2018 for soccer and baseball, and 2018 for basketball and volleyball.Significant RAEs were observed among soccer and baseball players in 1993, 2001, 2010, and 2018, and strong tendencies of RAEs were found among basketball and volleyball players in 2010 and 2018. The magnitudes of the RAEs in soccer, baseball, and volleyball decreased over time, but not in basketball.The exact reasons for the decreasing or unchanging RAEs among these professional players remain unclear, but socio-cultural factors, such as low birthrates and the popularity of sports in Japan, might be related to the changing RAEs. Significant RAEs were observed among Japanese professional soccer and baseball players registered in 1993, 2001, 2010, and 2018.The magnitude of RAEs in soccer, baseball, and volleyball decreased over time.Socio-cultural factors, such as low birthrates and the popularity of sports in Japan, might be related to the changing RAEs.For over a quarter of century, the “relative age effect” (RAE) has been investigated as one of the factors influencing sporting success. Relatively older children in a particular age group are more likely to achieve sporting success, compared with relatively younger children. Within the same age category, there can be a difference of almost a full year between the oldest and youngest children. RAEs have been confirmed in many sports, including soccer , 2, baseIn addition to physiological and psychological factors, the competition principle has also been considered as an important factor affecting RAEs. According to Musch and Grondin , “CompetHowever, after a thorough literature search, we do not know of any study conducting historical analysis of recent generations of RAEs in a given country, rather than the beginning of RAEs –22. MoreN = 3490 males) who played in the Japan Professional Football League (J-league) registered in 1993, 2001, 2010, and 2018 were extracted from official publications [N = 3096 males) who played in Nippon Professional Baseball (NPB) registered in 1993, 2001, 2010, and 2018 were extracted from official publications [N = 586 males) who played in the Japan Professional Basketball League (B-league) registered in 2010 and 2018 were extracted from an official publication [N = 633 males) who played in the Japan Volleyball League (V-league) registered in 2010 and 2018 were extracted from an official website [As mentioned above, soccer, baseball, basketball, and volleyball are very popular among male Japanese elementary and junior high school students . Therefoications –27. Dataications –30. Datalication  and a prlication . Data on website  and a pr website . Each leProfessional players were divided into four groups based on their month of birth: Q1 (April–June), Q2 (July–September), Q3 (October–December), and Q4 (January–March of the following year). Foreign players in the Japanese professional leagues were excluded because they had not passed through the Japanese school system. Chi-squared tests were applied to each group according to the four quarters to assess the significance of deviations from the expected number of births in each quarter. In line with previous studies , 33, 34,n = the number of observations.where The odds ratio (OR) and 95% confidence intervals were then calculated to provide additional information for both quartile and half-year distributions, following a previous review article . The ORsIn the present study, we focused on only male players, because the number of female players was small. For example, there are only 4 female professional baseball teams in Japan, and the professional league only started in 2010 . No femap < 0.05, and the p values are shown as p < 0.05, p < 0.01, and p < 0.001.Statistical tests were performed using computer software . Significance was set at Table Table Table In the present study, an historical analysis involving the most recent quarter-century (1993–2018) on RAEs among Japanese professional athletes was performed to clarify how the RAEs changed over time.Relatively older players i.e., Q1 players) may have greater opportunities for selection and experience in childhood because they are naturally heavier, taller, stronger, and faster; have greater endurance; and are more coordinated than relatively younger players during childhood , 16, 38, players One of the possibilities is that the popularity of sports has decreased annually. Table Next, we have to consider why the effect size of RAEs among soccer, baseball, and volleyball players decreased over time. As mentioned above, it is unlikely that efforts to reduce the RAEs were made nationwide. Therefore, other factors need to be considered. We proposed the acceleration of the age of peak height velocity (APHV) over time. Hermanussen et al.  reportedAs a limitation of the present study, our finding might be specific to Japan, because many activities, sports-related or academic, are based on a unique cutoff date (April 1), which is not the case in other countries. Data on RAEs with an historical analysis should be examined in other countries.The present study comprised an historical analysis to clarify the characteristics of RAEs among Japanese professional soccer, baseball, basketball, and volleyball players over time. Significant RAEs were observed among soccer and baseball players in 1993, 2001, 2010, and 2018, and strong tendencies of RAEs were found among basketball and volleyball players in 2010 and 2018. In addition, the magnitudes of RAEs among soccer, baseball, and volleyball decreased over time, but those of basketball did not. The exact reasons for these remain unclear, but socio-cultural factors, such as low birthrates and the popularity of sports in Japan, might be related to the changing RAEs.Additional file 1. Supplementary Table 1. The number of the general population  for calculating the age range.


The present review deals with the stages of creation, methods of calculation, and the use of a genetic risk score for coronary heart disease in various populations. The concept of risk factors is generally recognized on the basis of the results of epidemiological studies in the 20th century; according to this concept, the high prevalence of diseases of the circulatory system is due to lifestyle characteristics and associated risk factors. An important and relevant task for the healthcare system is to identify the population segments most susceptible to cardiovascular diseases (CVDs). The level of individual risk of an unfavorable cardiovascular prognosis is determined by genetic factors in addition to lifestyle factors. Cardiovascular diseases (CVDs) play a leading role in terms of adverse effects on life expectancy and mortality globally . The conCardiovascular risk is the likelihood of one or another adverse cardiovascular event (including death from CVD or complications) over a period of time. In clinical and research practice, several instruments are used to assess the total risk of cardiovascular pathology , and Prospective Cardiovascular Munster Study (PROCAM)).The Framingham Risk Scale was proposed according to the results of the longest prospective study  conducted in the United States, which included 5209 people. This tool allows to assess 10-year risk of fatal and nonfatal cardiovascular complications, and this risk has 4 grades: low (risk of complications less than 10%), medium (risk greater than 10% and less than 20%), high (>20%), and very high (>30%). To calculate the total score, 5 factors are taken into account: unmodifiable (sex and age) and modifiable ones  . This riThe SCORE  scale was created in Europe in 2003 on the basis of 12 cohort studies and data on 205,178 patients . It has The PROCAM (Prospective Cardiovascular Munster Study) scale was developed from the results of a prospective study, PROCAM , which began in 1979. The study involved 21,306 people  [A family history of relevant diseases is of no small importance for assessing cardiovascular risk. Risk calculators addressing the contribution of genetic factors are under development ,5,6,7,8.The present review deals with the stages of creation, methods of calculation, and the use of a genetic risk score (GRS) for coronary heart disease (CHD) in various populations.In 2005, Horne B.D. et al. described a method for calculating a GRS for predicting CHD, and this tool was based on a combination of genotyping data on single-nucleotide polymorphisms (SNPs) . That stIn subsequent years 2008–2015), studies had been carried out with different variations of the GRS and indicated correlations with CHD, MI, or other CVDs  [4,5,6,7, studiesTo obtain estimates of the risk of CVDs in individuals by means of the GRS, the Cox proportional hazards model and logistic regression analysis have been used mainly ,45,46,47Discrimination and reclassification have been the criteria for GRS quality in such studies . DiscrimAntiochos P. et al. have evaluated prognostic power of a combination of a family history of diseases and GRSs  for CHD cases among whites from a prospective study, CoLaus. After adjustment for cardiovascular RFs, both the family history and weighted polygenic GRSs were found to be associated with CHD, both jointly and separately, and provided additional information for predicting coronary events .Tada H. et al. have tested whether the prognostic ability of a GRS for CHD would improve with an extension of GRS-27 published in 2015 by Mega J.L. et al. .p = 0.037). For GRS-153, the association with relapses of ACS and/or mortality was not confirmed; however, an association of GRS-32 with a three-vessel lesion was found, and, for GRS-47, an inverse correlation with smoking and MI featuring ST elevation was revealed [Vaara S. et al. have created 3 partially overlapping GRSs  and tested their prognostic ability for a relapse of acute coronary syndrome (ACS). For GRS-47, a significant association with ACS recurrence was confirmed regardless of clinical factors  .Despite the high prevalence of cardiovascular RFs, such as hypertension, diabetes mellitus, and obesity, Afro-Caribbean mortality from CHD is lower than that of whites. Larifla L. et al. have evaluated the association of two GRSs—consisting of overlapping sets of 19 and 14 SNPs previously associated with CHD in whites—with CHD in Afro-Caribbean countries. This study indicated that the tested multilocus GRSs are strong predictors of this disease in Afro-Caribbeans. Significant differences in the allele distribution of 17 SNPs were noted between the two ethnic groups (Afro-Caribbeans and whites) .Abraham G. et al. have created a GRS of 49,310 SNPs based on a meta-analysis by the CARDIoGRAMplusC4D Consortium and tested it on five prospective population cohorts (three FINRISK and two Framingham Heart Study cohorts). For the studied GRS, an association with CHD was found in two cohorts . Integration of the GRS with the Framingham Risk Score or ACC/AHA13 improved 10-year risk prediction, especially for people over 60. The high genomic risk was partially offset by low systolic blood pressure and cholesterol levels as well as by nonsmoking status. Those authors concluded that a GRS based on a large number of SNPs improves the risk assessment of CHD .Joseph P.G. et al. have investigated the contribution of a GRS—consisting of 25 SNPs associated with CHD in Europeans and South Asians—to MI risk in various populations, with six ethnic groups as an example . The GRS was significantly associated with MI with minimal observed heterogeneity in Europeans, South Asians, Southeast Asians, and Arabs but not in Hispanics and Africans. In the general cohort, there were only minor changes in the prognostic ability as compared to clinical factors alone .Sotos-Prieto M. et al. have examined the interaction and presence or absence of synergy between the Lifestyle Cardiovascular Risk Score (LCRS) and a GRS (14 SNPs associated with MI) in the adult Hispanic/Latino population of Costa Rica. Odds ratios (ORs) for MI were 2.72 (95% CI: 2.33–3.17) per LCRS unit and 1.13 (95% CI: 1.06–1.21) per GRS unit, and there was a significant joint association for high tertiles of the GRS and LCRS as compared to low tertiles. According to those authors, improvements in a lifestyle can help prevent MI regardless of genetic predisposition .Amit V. Khera et al. have estimated the extent to which a healthy lifestyle can offset an increased genetic risk of CHD. The relative risk of new coronary events in participants with high genetic risk was 91% higher relative to those with low genetic risk. Regardless of the category of genetic risk, with a healthy lifestyle , the risk of coronary events was significantly lower than that with an unhealthy lifestyle . In all four samples with a total of 55,685 participants drawn from prospective cohorts of ARIC, the Women’s Genome Health Study (WGHS), the Malmö Diet and Cancer Study (MDCS), and the BioImage Study, genetic and lifestyle factors independently influenced the susceptibility to CHD. In participants with a high genetic risk, a healthy lifestyle decreased the relative risk of CHD by almost 50% as compared with an unhealthy lifestyle .Widespread genetic risk variants can contribute to the heritability of early CHD. Christiansen M.K. et al. have assessed the association of a GRS (45 SNPs) with age at CHD onset and examined the relation between the GRS, familial clustering, and severity of CHD at its early onset. A higher risk shown by the GRS was registered in patients with early-onset CHD compared with late CHD and controls, but the GRS did not correlate with the stratified log-rank family score, which was employed to assess family clustering. Nevertheless, there was a significant association of the stratified log-rank family score with CHD , suggesting that the analysis of a family history and TRFs are of greater clinical relevance than the GRS in question . AdditioShahid S.U. et al. have analyzed the genetic risk of CHD in individuals from Pakistan by means of a GRS consisting of 21 variants in 18 genes and determined whether this GRS is associated with blood lipid levels. The GRS was found to be associated with CHD, and the OR was statistically significantly higher in the top quintile than in the bottom quintile . A strong correlation of atherogenic blood lipids with the GRS was noted too: an increase in atherogenic and a decrease in atheroprotective lipids’ levels were shown to be associated with a higher GRS result .Fritz J. et al. in their study showed an association of a family CHD history and of the GRS-50 used by Tada et al.  with CHDPsychological stress is an independent RF of CVDs, but the mechanism via which stress is linked with CVDs is not well studied. The purpose of a study by Svensson T. et al. was elucidation of the association between (i) a GRS (50 SNPs) (with previously proven association with CHD ), indivip < 0.001), while in all other patients, the relative risk reduction was 24% , despite a similar decrease in LDL-C levels. Additionally, each increase in GRS by 1 standard deviation was associated with a 1.32-fold increase (95% CI: 1.04–1.68) in the likelihood of coronary artery calcification and a 9.7% increase (95% CI: 2.2–17.8) in the severity of a carotid plaque [On the basis of earlier research ,64,65, Nd plaque .p < 0.001) in patients with familial hypercholesterolemia (FH). These results indicate that even in a severe monogenic disease such as FH, a GRS can improve the risk assessment of CVDs, thereby possibly facilitating a more personalized approach to treatment [Paquette M. et al. have shown that a GRS containing 192 SNPs predicts CVDs in general and CHD in particular  in patients with established CHD. Study participants with CHD receiving statin therapy and with hypertension manifested an association between weighted GRS and MACEs. Such individuals with a medium or high risk according to the GRS also had a 2.138- and 4.048-fold higher risk of a MACE, respectively .p < 0.0001). The tested GRS added prognostic value to TRFs in all risk subgroups [Pereira A. et al. have evaluated the utility of adding a multilocus GRS to the Framingham Risk Score during the risk assessment of CHD. Multivariate analysis showed that the GRS is an independent predictor of CHD onset  as compared to a low-risk group [One of severe subtypes of CHD is left main coronary artery disease (LMCAD), which is associated with adverse clinical outcomes  and has sk group .For the primary prevention of CHD, Inoye M. et al. have constructed a GRS (metaGRS) based on 1.7 million SNPs identified by a meta-analysis and assessed its potential. All three GRSs tested—(1) 46,000 SNPs from Metabochip, (2) 202 SNPs significantly associated with CHD, (3) a whole-genome GRS from the 1000 Genomes Project—turned out to be associated with CHD . In an analysis of nearly 500,000 individuals from a prospective nationwide cohort study (UK Biobank), those authors evaluated a combined genomic risk metric (metaGRS) based on the summary statistics of the largest previous genome-wide studies on associations with CHD. They reported several discoveries that significantly advance the concept of applying genomic information to help stratify people at risk of CHD in general populations. MetaGRS yielded greater risk discrimination than did previously published genomic risk calculators based on selected SNPs. For instance, those authors found that metaGRS has a higher HR and positive predictive value at any given sensitivity as well as a 4-fold HR for CHD when comparing people in the upper and lower quintiles of the risk score distribution. Because these findings suggest that the higher genetic risk can at least in part be diminished by lipid-lowering and/or hypotensive therapy, people at high genetic risk may get the greatest benefit from starting these treatments early. On the other hand, the GRSs currently in use also predict the risk of CHD in people already on statin therapy for CHD, thus highlighting the need to develop new modalities to reduce the residual risk of the disease. At the same time, the genetically determined risk of CHD is mostly independent from common RFs such as blood lipids, blood pressure, and smoking. Therefore, the GRS developed in that study helps to significantly improve the stratification of people by CHD risk in various populations and shows potential usefulness of genomic screening at an early age in addition to traditional risk assessment techniques .p = 1.4 × 10−4). Nevertheless, those authors note that a panel of SNPs included in a GRS should be designed carefully, in particular, the predisposition to the disease in different ethnic groups should be taken into consideration [In another study, researchers analyzed four SNPs affecting coronary arteries and genotyped them in a Pakistani ethnic group. For each individual SNP, no statistically significant associations with CHD were found, in contrast to the GRS constructed from them  previously associated with CHD in Europeans among individuals of African American, Hispanic, and East Asian descent. In a comparison of the third tertile with the first, GRS-12 showed an association with an increased risk of CHD in African Americans and Hispanics but not East Asians . For GRS-51, an association was identified for Hispanics and East Asians but not for African Americans . In a meta-analysis, both GRSs proved to be associated with the risk of CHD with a similar effect size [In developing countries, the average age at the first signs of CHD  typically exceeds 60 years . Neverthp = 0.0002). Besides, in the European group, FDR-267 predicted CHD (C-statistic = 0.60) but showed no improvement over clinical RFs . The predictive power of FDR-267 for CHD was lower in African Americans. Consequently, FDR-267 was significantly associated with CHD in the European sample, with an effect size comparable to that of the family history. FDR-267 discriminated individuals with and without CHD but did not improve the risk assessment of CHD as compared to clinical variables alone [Liu R. et al. have devised a GRS called FDR-267  from markers that were significantly associated with CHD in a meta-analysis of the UK Biobank cohort with an FDR < 5%. FDR-267 was tested on the ARIC cohort in European and African American groups. GRS FDR-267 turned out to be associated with an increase in odds ratio (OR) and HR in the European population: 1.45 (95% CI: 1.39–1.51) and 1.32 (95% CI: 1.26–1.38), respectively, which slightly improved AUC after addition to the clinical model  with CHD, TRFs of CHD, and nonvascular diseases  that have previously proven a genetic contribution of magnitude similar to that of CHD. The GRS was found to be associated with 22 traits, including RFs, diseases secondary to CHD, and comorbid and non-CVD conditions. Sensitivity analyses were performed on individuals without CHD or with stable angina pectoris to determine the relation of the CHD-associated GRS with genetic susceptibility to these 22 traits. Hypercholesterolemia  and hypertension  proved to be closely associated with the GRS, indicating that this rating scale contains genetic variants predisposing to these conditions. Nevertheless, the GRS was also significantly associated with CHD cases that lacked TRFs . The study also uncovered significant associations between this GRS and peripheral arterial disease , abdominal aortic aneurysm , and stroke ; these associations remained significant in a sensitivity analysis under the assumption of a general genetic predisposition. GRS was also found to correlate with heart failure , atrial fibrillation , and premature death . These associations did not survive a sensitivity analysis, which showed that they were secondary to CHD. There was an inverse correlation between the GRS and migraine . Therefore, a wide range of CVDs, including premature death, can develop sequentially or in parallel with CHD against the same genetic background .p = 0.001), mainly for cardiovascular reasons . These patients had a significantly increased risk of death from CVDs  but not the risk of death of other causes. These results indicate that a genetic predisposition to CHD allows one to predict the severity of hospitalization and mortality, especially that of cardiovascular causes, regardless of TRFs [Sjögren M. et al. have tested the predictive ability of the GRS (50 SNPs) developed by Tada  for CHD  of TRFs .The purpose of a study by Severance L.M. et al. was to determine the optimal age for screening for coronary artery calcification by means of a GRS  in a mup = 0.04) for the moderate-risk group and 20.7  for high-risk groups, where LDL-C concentration was ≥2.8 mmol/L (≥110 mg/dL). The inclusion of the GRS also bettered the C-statistic (ΔC-statistic = 0.086), NRI (30%), and integrated discrimination improvement (0.05) [Rincon L.M. et al. have investigated whether a GRS improves the prognosis of recurrent cardiovascular events  in patients <55 years of age with acute MI without concomitant diabetes mellitus. These researchers also tested whether this GRS detects a more aggressive type of atherosclerosis. Compared to the general population, study participants manifested higher prevalence of risk alleles (9 of 11). A significant correlation of the GRS with recurrence of cardiovascular events was found, especially in individuals with initially elevated LDL-C levels. Compared to the low-risk GRS tertile, the multi-adjusted HRs for recurrence were 10.2 (95% CI: 1.1–100.3. p = 0.009) and in patients without pathogenic variants associated with FH  after adjustment for TRFs. The GRS correlated with greater magnitude of subclinical atherosclerosis (p = 0.039). Patients with FH who are at high GRS risk may benefit from a more active intervention, including lifestyle changes and aggressive lipid-lowering therapy. Further evaluation of the usefulness of GRSs, including their role in the management of patients with FH in the clinic, requires a study on prospective cohorts [Earlier studies suggest that the severe monogenic disease FH is associated with an increased risk of CHD , but ris cohorts .p < 0.001: cardiac arrest of cardiac etiology), and the magnitude of the risk detected by the GRS was comparable and in many cases even greater than that detected by TRFs. Given the high mortality rate of the cardiac arrest of cardiac etiology, those authors suggest that clear stratification into high-, medium-, and low-risk groups according to a GRS may help to decide on a treatment strategy, for example, prescription of lifelong statin therapy. In addition, because cardiac arrest of cardiac etiology is primarily caused by ventricular fibrillation or ventricular tachycardia, cardioverter-defibrillator implantation may be indicated for high-risk individuals as a primary prevention measure [Ohlsson M.A. et al. have studied the predictive ability of a GRS—previously used in the work of Tada H. et al. —regardin measure .The purpose of a study by Mosley J.D. et al. was to compare CHD-prognostic power of a GRS with that of TRFs, and the genetic risk was calculated via a GRS including 6,630,149 SNPs. The GRS under study was significantly associated with a 10-year incidence of CHD in cohorts ARIC  and Multi-Ethnic Study of Atherosclerosis . The addition of the GRS to a risk calculator called “pooled cohort equations” did not significantly increase AUC in either cohort . Furthermore, there was no significant improvement in reclassification in either the ARIC sample  or the MESA sample . Those authors propose that the assessment of polygenic risk may become a motivating factor for starting a healthy lifestyle; however, they believe that there are simpler ways to promote this change .Elliott J. et al. have investigated whether a GRS can improve the assessment of CHD risk as compared to the pooled cohort equations tool, which is affected by sex, race, age, blood pressure, total cholesterol, LDL-C, smoking status, and a history of diabetes mellitus. Their study involved two samples: (1) a case-control sample to optimize the predictive efficiency of CHD polygenic risk assessment on the basis of published genome-wide association studies (GWASs); (2) a prospective cohort was used to determine the predictive accuracy of the polygenic risk assessment, of the pooled cohort equations, and of a combination for incident CHD. In a cohort of 352,660 participants on whom predictive accuracy of the tested models was assessed, 6272 incident cases of CHD were registered during a median of 8 years of observation. CHD discrimination by the GRS, by the pooled cohort equations, and by their combination resulted in a C-statistic of 0.61 (95% CI: 0.60–0.62), 0.76 (95% CI: 0.75–0.77), and 0.78 (95% CI: 0.77–0.79), respectively. The change in the C-statistic between the last two models was 0.02 (95% CI: 0.01–0.03). Adding the GRS to the pooled cohort equations resulted in a 4.0% improvement in the overall NRI (95% CI: 3.1–4.9%)). The addition of the GRS to the pooled cohort equations tool resulted in a statistically significant but only modest improvement in prognostic accuracy for incident CHD and an improvement in risk stratification for only a small proportion of the subjects. Thus, the addition of genetic information to the pooled cohort equations tool requires further research before possible introduction into clinical practice .p = 0.003), with this association unabated after adjustment for TRFs. The addition of the GRS to prognostic models for seven clinical RFs and EPICOR slightly improved risk stratification for MACEs . Decision curve analysis suggested that adding the GRS to the clinical factors increased CHD-predictive power when compared to each tool alone. The GRS proved to be associated with MACEs after multiple adjustment in a cohort including Chinese ACS patients [Jiang J. et al. have determined whether a GRS consisting of 79 CHD-associated SNPs can predict the risk of MACEs in an ACS cohort in a prospective study of a median duration of 2 years. In the age- and sex-adjusted model, each increase in the GRS by a standard deviation was associated with a 33% increase in the risk of CVDs , CHD and MI (PHACTR1) and genes associated with lipid metabolism , myogenesis (VAMP5), an immune response (IL6R), and angiogenesis . The table presents both variants with low prevalence  = 0.02 (G) for rs10455872) and widespread variants with MAF > 0.3, which make up 30% of the SNPs included in the table. The majority of the single-nucleotide variants listed in the table are located in introns.Thus, most authors have reported an improvement in prognostic power when a GRS is added to existing CVD risk calculators; however, some authors have not confirmed  the association between a GRS and CVDs ,42,53,77For 15 years (2005–2020) in the studies examining the association between a GRS and cardiovascular events, the methods have not undergone significant changes. Cox’s proportional hazards model and logistic regression analysis as of October 2020 remain among the most popular techniques for finding a relation between a disease and genetic factors. To measure survival, both Cox’s proportional hazards model and the Kaplan–Meier method have been used . The assThe evolution and improvement of the predictive ability of GRSs with the increasing number of SNPs in the model can be traced using the works of Abraham G. et al.  and InoyAlthough applied medical research will be necessary to properly assess the clinical utility of GRSs for CHD, areas of potential clinical application can already be foreseen. For instance, genotyping across large parts of the genome requires a one-time cost of approximately USD 50 in 2018 prices  and can Designing a GRS for the assessment of cardiovascular risk is an important task for modern cardiology. Given that the risk of CVDs as assessed by GRSs is preventable or at least can be partially reduced , early diagnosis may improve the quality and duration of life of the patients and should reduce economic costs.