The pool of primordial follicles formed in the ovaries during early development determines the span and quality of fertility in the reproductive life of a woman. As exposure to occupational and environmental toxicants (ETs) has become inevitable, consequences on female fertility need to be established. This review focuses on the ETs, especially well-studied prototypes of the classes endocrine disrupting chemicals (EDCs), heavy metals, agrochemicals, cigarette smoke, certain chemicals used in plastic, cosmetic and sanitary product industries etc that adversely affect the female fertility. Many in vitro, in vivo and epidemiological studies have indicated that these ETs have the potential to affect folliculogenesis and cause reduced fertility in women. Here, we emphasize on four main conditions: polycystic ovary syndrome, primary ovarian insufficiency, multioocytic follicles and meiotic defects including aneuploidies which can be precipitated by ETs. These are considered main causes for reduced female fertility by directly altering the follicular recruitment, development and oocytic meiosis. Although substantial experimental evidence is drawn with respect to the detrimental effects, it is clear that establishing the role of one ET as a risk factor in a single condition is difficult as multiple conditions have common risk factors. Therefore, it is important to consider this as a matter of public and wildlife health. The story of gametogenesis and sex-specific gonadal formation starts with the origin of pluripotent primordial germ cells (PGC) in the extraembryonic tissue followed by their migration towards the gonadal ridge. This involves various signals between the PGCs, somatic cells of hind gut as well as primordial gonads. Major signaling molecules involved in the maintenance of pluripotency and migration include bone morphogenetic protein (BMP), Wnt3, Kit ligand (KL), etc. , heavy metals, pesticides, cigarette smoke, certain chemicals used in plastic, cosmetic and sanitary product industries can adversely affect female fertility Barker . EndocriAll the above can act as potential risk factors, for several conditions that affect the female fertility and fecundity, especially on a prolonged exposure. Since it is not possible to cover all the effects of every ET on female fertility, this review mainly focuses on the prototypes of the above classes of ETs that have been extensively studied and screened either in vitro, in vivo or in humans for causing fertility-related problems by precipitating polycystic ovary syndrome (PCOS), primary ovarian insufficiency (POI), multioocytic follicles (MOF) and meiotic defects including aneuploidies.. have been proven to exert detrimental effects on ovaries causing various reproductive problems. One class of such compounds are the EDCs or endocrine disruptors. Most EDCs are man-made chemicals. They are found in routinely and frequently used materials such as agrochemicals, personal care products, plastics, metals and additives or contaminants in certain food (WHO-EDC). EDCs can also be found in drinking water, food, air and diet  2.5 were assessed. The study reported that women exposed to the pollutants were at a higher risk of PCOS  of MOFs throughout the reproductive life differ from species to species . DOR can lead to primary ovarian insufficiency. Various terms like premature ovarian insufficiency, premature ovarian failure and premature menopause are used synonymously with primary ovarian insufficiency (POI). However, more accurate term for the condition is primary ovarian insufficiency Welt . In primVarious environmental factors are recently found to interfere with the ovarian function causing POI. ETs have shown to deplete the ovarian reserve and the depletion is linked to prenatal, postnatal and later stages of life. It has also been observed that the most sensitive stage of follicular development with environmental toxicant exposure is variable , a chlorinated aromatic compound used as a broad-spectrum antibiotic in cosmetics, veterinary, plastic industries and household products, was found to affect the ovarian reserve in women. It is considered as one of the toxicants present in water. Although its action on skin from cosmetic products is short, after it is released into the sewage water, it cannot be completely removed through wastewater treatment. Its by-products like methyltriclosan and other chlorinated phenols pose a higher toxicity problem than the parent chemical itself (Dann and Hontela .(2015) have shown that diethylhexyl phthalates (DEHP) has accelerated the follicular recruitment in the offspring mice at puberty when the maternal mice were exposed to DEHP during pregnancy. Interestingly, there was a rapid reduction in the primordial follicle pool even in the F2 generation of mice suggesting that the insufficiency due to DEHP can also be transgenerational. This can be a worrisome issue because, humans are exposed to phthalates on a daily basis, as they are very widely used in plastic and cosmetic industries. Another study reported an association between the urinary DEHP metabolite level with a decrease in AFC. The authors found that DEHP can adversely affect the pool of growing antral follicles and younger women appeared to be at a higher risk. Whether this diminishment of antral follicle pool was a result of decline in primordial follicles was not proven  showed that the effects of DEHP in mice follicles were transgenerational.In a study by Park et al., the aut.. Conside.. Zhang eCigarette smoke, apart from posing a risk for PCOS, has also shown to significantly decline the ovarian reserve. Exposure to cigarette smoke in relation to diminishment of ovarian reserve, manifestation of POI and premature menopause was established very early in the literature (Harlow and Signorello .Cadmium (Cd) is a heavy metal toxicant widely used in manufacturing batteries, metal smelting, municipal waste incineration etc. It is also found in cigarette smoke 2 but unaltered in cumulus-free oocytes . The rate of germinal vesicle breakdown (GVBD) was examined both in cumulus-enclosed and cumulus-free oocytes and the rate of GVBD of cumulus-enclosed oocytes was found to be reduced significantly with 10fM Pb(NOWe have made a considerable progress in drawing substantial experimental evidence with respect to the detrimental effects of ETs on female fertility. Nevertheless, there is still an ocean of things to explore so as to understand the molecular mechanisms involved. Although the effects of toxicants like BPA and phthalates have been extensively studied, there may be many other toxicants that persist in the environment, the upshots of which remain unprecedented and unexplored till date. For instance, determining the effect of ETs on meiosis remains obscure owing to the fact that either the aftermath is hard to detect or their influence is often neglected. Similarly, it is difficult to establish the role of a single ET as a risk factor in conditions like PCOS and POI where multiple causative factors are involved. Understanding the implications of ETs on other facets like increase-in-time-to-pregnancy and idiopathic infertility is crucial. Therefore, reliable models that eliminate confounding factors and create well-founded evidence are crucial. Many more epidemiological studies in addition to experimental research are pivotal, and such studies must be encouraged. Projects like ‘European Human Biomonitoring Initiative’ (Hbm4eu.eu


Background: Disruption in cerebrovascular reactivity following traumatic brain injury (TBI) is a known phenomenon that may hold prognostic value and clinical relevance. Ultimately, improved knowledge of this process and more robust means of continuous assessment may lead to advances in precision medicine following TBI. One such method is transcranial Doppler (TCD), which has been employed to evaluate cerebrovascular reactivity following injury utilizing a continuous time-series approach.Objective: The present study undertakes a scoping review of the literature on the association of continuous time-domain TCD based indices of cerebrovascular reactivity, with global functional outcomes, cerebral physiologic correlates, and imaging evidence of lesion change.Design: Multiple databases were searched from inception to November 2020 for articles relevant to the association of continuous time-domain TCD based indices of cerebrovascular reactivity with global functional outcomes, cerebral physiologic correlates, and imaging evidence of lesion change.Results: Thirty-six relevant articles were identified. There was significant evidence supporting an association with continuous time-domain TCD based indices and functional outcomes following TBI. Indices based on mean flow velocity, as measured by TCD, were most numerous while more recent studies point to systolic flow velocity-based indices encoding more prognostic utility. Physiologic parameters such as intracranial pressure, cerebral perfusion pressure, Carbon Dioxide (CO2) reactivity as well as more established indices of cerebrovascular reactivity have all been associated with these TCD based indices. The literature has been concentrated in a few centres and is further limited by the lack of multivariate analysis.Conclusions: This systematic scoping review of the literature identifies that there is a substantial body of evidence that cerebrovascular reactivity as measured by time-domain TCD based indices have prognostic utility following TBI. Indices based on mean flow velocities have the largest body of literature for their support. However, recent studies indicate that indices based on systolic flow velocities may contain the most prognostic utility and more closely follow more established measures of cerebrovascular reactivity. To a lesser extent, the literature supports some associations between these indices and cerebral physiologic parameters. These indices provide a more complete picture of the patient’s physiome following TBI and may ultimately lead to personalized and precise clinical care. Further validation in multi-institution studies is required before these indices can be widely adopted clinically. The disruption of cerebral autoregulation (CA) following traumatic brain injury (TBI) has been known since the 1970s . At that2) reactivity and even CA  in 1982 as a non-invasive means of evaluating CBF through insonation of flow velocities (FV) in the basal arteries of the brain . Since t even CA . Unfortu even CA .TCD was first described as a tool to continuously evaluate CA following TBI by Czosnyka and colleagues in 1996 . In thisSince then, a significant amount of research has been undertaken to examine the association between continuous TCD based indices of cerebrovascular reactivity and outcomes following TBI (It should be noted that cerebrovascular reactivity and CA are not entirely interchangeable as cerebrovascular reactivity can occur outside the limits of autoregulation. Cerebrovascular reactivity is the broader term that describes the physiologic process that is measured by these indices). Slightly modified indices that used the diastolic and systolic FV were examined  along with indices that utilized ABP, as opposed to CPP, as the driving force . A systematically conducted scoping review of the available literature was conducted based on the methodological framework described by The review questions and search strategy were decided upon by the senior author (FAZ) and primary author (AG)The question of this systematically conducted scoping review was: What is the association of continuous time-domain TCD based indices of cerebrovascular reactivity, with: A. global functional outcomes, B. cerebral physiologic correlates, and C. imaging evidence of lesion change following moderate-to-severe TBI?All English language studies, either prospective or retrospective, with 20 or more adult (age 18 years or older) moderate and severe TBI patients, were included. Moderate TBI is defined as an admission Glasgow coma scale (GCS) of 9–12 while a GCS of 3–8 defines severe TBI.Only continuous time-domain TCD based cerebrovascular reactivity metrics were of interest, excluding both intermittent techniques  and thos2), and CO2 reactivity were also examined. Give that the pressure reactivity index (PRx) has become a widely accepted continuous measures of cerebrovascular reactivity , midline shift, and hematoma volume as well as the development on new lesions were all considered relevant.Exclusion criteria for studies were the following: non-English, non-human, non-TBI, mild TBI, paediatric cohorts, non-time-domain TCD based indices, non-continuous TCD metrics, cohort < 20 TBI patients, or no relevant outcome  association. Review studies and meta-analysis were also excluded from consideration.BIOSIS, Cochrane Library, EMBASE, MEDLINE, and SCOPUS were searched from inception to November 2020 using individualized search strategies for each database. The search strategy for SCOPUS can be seen in Using two reviewers (AG and LF) a two-step review of all articles returned by our search strategies was performed. In the first filter phase, each reviewer independently screened all studies identified using the above-described search strategy and determined if they met the inclusion criteria based on their title and abstract. The resulting list of studies was then passed through the second filter phase where once again each reviewer independently determined if the studies met the inclusion criteria, but this time based on the full text. Any discrepancies between the two reviewers were resolved by a third party (FAZ).Data was extracted from the selected articles and compiled into various data fields. These fields included the following: number of patients, study design, institution, mean age, mean GCS on presentation, number of male patients, additional patient characteristics, goals of the study, indices examined, duration of insonation, outcomes evaluated, key results, and conclusions.Given the goal of this review was to provide a comprehensive scoping overview of the available literature, a formal bias assessment was not conducted.Due to the heterogeneity of the results/study design no meta-analysis was performed.The results of the search and filtration strategy can be seen in There is strong evidence of an association between dysfunctional cerebrovascular reactivity, as measured by continuous time-domain TCD based indices, and mortality following TBI, with numerous studies finding a higher Mx correlating with mortality . A notabThe evidence for the association between other TCD based indices and mortality is not as clear with a 2018 study by Zeiler and colleagues having most comprehensively examined this relationship in a cohort of 281 TBI patients . Sx and Most studies chose to use Glasgow Outcome Scale (GOS) as their outcome metric of choice, while opting to dichotomize outcome into favourable/unfavourable or good/poor . Follow up was usually collected at 6 months post-injury. Once again, Mx seems to have the strongest body of evidence supporting its association with outcomes with studies consistently finding that a higher Mx was associated with poor or unfavourable outcome at follow up . When MxOnce again, the evidence supporting other TCD based indices is not as prevalent. Mx_a was found to be associated with global functional outcomes in four different articles with higher values being associated with worse outcomes . Dx and Thresholds values of indices, where outcomes or mortality significantly increases, was the focus of 4 articles. In a 2002 study by Czonyka and colleagues they found a threshold for Mx of 0.23, above which mortality went from 11 to 47% . A similIn the 2011 study by Sorrentino and colleagues, Mx_a was found to have a threshold at 0.3 where both mortality and functional outcome both worsened in their cohort . Zeiler TCD can be utilized to insonate the left and right MCA of the patient without any increased risk to the patient and so a number of studies examined the effect of hemispheric asymmetry of TCD based indices of cerebrovascular reactivity. Czosnyka and colleagues noted in their 2003 study that Mx was worse on the side of contusion or expansion if the patient had midline shift. They also noted that a hemispheric asymmetry was significantly more common in patients that died than those that survived . In a foCerebrovascular reactivity is often measured by the fluctuation of ICP in response to changes in ABP in a more established index known as the pressure reactivity index (PRx). There has been a building body of evidence supporting cerebrovascular reactivity, in the form of PRx, being an important physiologic measure in TBI and so several studies have assessed the covariance of PRx and TCD based indices . Once agThe relationship between TCD based indices and ICP seems to be somewhat independent with Mx being found to be positively correlated with ICP in early studies and a notable increased in Mx with ICPs above 20 mm Hg . Notably2 reactivity  instrument. Assessments such as these would have provided a more comprehensive understanding of a patient’s health-related quality of life following their injury . AdditioAnother weakness is in the analysis performed in these studies. We have seen that these indices are related to age, severity of injury and ICP , howeverThe literature also seems to mainly focus on Mx as the TCD based index most studied. This is likely due to it being the first TCD based index described . While tThere are also some limitations inherent to TCD that become apparent when reviewing the literature. Due to the difficulty in obtaining prolonged recordings of TCD data, most studies report only data collection during a small proportion of time spent in ICU with some studies reporting as little as 10 min of insonation a day  and no sFinally, there seems to be an absence of any literature evaluating the link between cerebrovascular reactivity, as measured by time-domain TCD based indices, and the evolution or progression of imaging findings in the setting of TBI. Similar reports have been published with regards to PRx and have found a link between cerebrovascular reactivity and lesion progression following TBI . A link This scoping review does, in and of itself, has some limitations. Articles included in this study were limited to those that contained cohorts of greater than 20 TBI patients. This was done in order to limit the prevalence of small case series and case reports, however, this may have limited the diversity of institutions included in this review. Additionally, this review was limited to continuous time-domain TCD based indices at the exclusion of frequency domain based TCD indices. While this was done to avoid excessive heterogeneity in indices examined, there is evidence that these indices do associate with outcomes following TBI, but to a lesser degree . AdditioThis review highlights some key areas for further development. First, before TCD based indices are adopted clinically, validation of these findings will need to occur on more diverse datasets. Large multi-institutional/multi-national collaborative networks such as the ones found in the CENTER-TBI study and CAnadian High-Resolution TBI (CAHR-TBI) Research Collaborative are ideal for such validation studies . In ordeThe clinically applicability of these indices may also be expanded past prognostication. Further exploration of utilizing TCD based indices to identify personalized CPP targets in critically ill patients following TBI must be undertaken. Precision medicine with personalized targets derived from these indices may provide the means to significantly alter outcomes following TBI and is already being explored utilizing more established measure of cerebrovascular reactivity, such as PRx .Further investigation of TCD based indices, aside from Mx, should also be done given the evidence of the strength of correlation with outcome Sx has demonstrated. Additionally, ABP based indices are of particular interest given the recent description of entirely non-invasive methods of collecting the requisite physiologic data .Finally, advances in TCD technology, such as robotic TCD has allowed for prolonged recording in and above 4 h due to the continuous robotic optimization of probe position. This has already been described as a means of assessing cerebrovascular reactivity for extended periods of time and may extend the percentage of time monitored while in ICU . This maThis systematic scoping review of the literature identifies that there is a substantial body of evidence that cerebrovascular reactivity as measured by time-domain TCD based indices have prognostic utility following TBI. To a lesser extent, the literature has also explored some associations between these indices and cerebral physiologic parameters in this patient population. Notably, there is lack of evidence evaluating the correlation between these indices and radiographic progression following injury. Further research needs to be done to expand the generalizability of these results and identify optimal inputs and collection methods for these indices before they can be widely clinically adopted. However, their role in precision medicine following traumatic brain injury is promising.


Field notes (FNs) are used in Family Medicine residency programs to foster reflective learning and facilitate formative assessment. Residents assess their strengths and weaknesses and develop action plans for further improvement. This study explored the use of FNs in the University of Manitoba’s Family Medicine residency program 5 years after their implementation.This multi-method study examined 520 FNs from 16 recent graduates from the University of Manitoba Family Medicine residency program. Quantitative analysis (frequencies and means) enabled descriptions and comparisons between training sites. Four themes emerged from inductive content analysis highlighting common ideas reflected upon.Patient-Centered Care, Patient Safety, Achieving Balance, and Confidence) were identified from qualitative analysis of residents’ narrative notes.Residents displayed cyclical variation in the FN generation over 2 years. Eight of the 99 Priority Topics  were not captured in this data set. The domains of Care of First Nations, Inuit, and Metis; Care of the Vulnerable and Underserved; and Behavioural Medicine and the CanMEDS-FM roles of FM – Procedural Skill, Leader/Manager, and Professional were less frequently reflected upon. Four themes (Vygotsky’s Sociocultural Theory of Cognitive Development was proposed as a lens through which to examine factors influencing resident learning. Residents’ discomfort with certain topics may lead to avoidance in reflecting upon certain competencies in FNs, impacting skill acquisition. Further research should explore factors influencing residents’ perceptions FNs and how to best assist residents in becoming competent, confident practitioners. Canadian Family Medicine residency programs are entrusted with ensuring the provision of competency-based education so as to prepare Family Medicine residents to take on the role of generalists. Residents must develop competence in managing a wide range of patient concerns and pathologies over a relatively short two-year residency. As such, educators are tasked with multiple challenges in ensuring well-rounded and impactful educational experiences , 2. EducFamily Medicine education in Canada continues to evolve to meet residents’ and society’s needs. Ensuring the competence of residents has always been a prominent goal of residency programs as Family Medicine educators continually seek to optimize educational opportunities that will best prepare residents to be care providers across the lifespan of their patients by the end of the two-year program , 3. The One tool used to promote reflective practice is the Field Note (FN). FNs are short reflective narratives authored by residents to encourage self-reflection and feedback by preceptors , 5–10. TCanadian Family Medicine residency education is comprised of 2 years of clinical and didactic training. At the University of Manitoba, the Family Medicine residency program is divided into nine streams which fall into three general groups: Urban, Rural, and Northern Remote –14. Withthe frequency and trends in the use of FNs by residents and/or their supervisors over the training period and between urban versus rural sites,the content  captured by FNs during the training period,the extent of CFPC defined “skill dimensions” addressed in the residents’ clinical experiences,the nature of their learning experiences, self-assessments and action plans.Although residents have always been provided the opportunity to review their progress with their preceptors, prior to implementing FNs, they were not required to formally self-evaluate. One of the requirements for life-long learning is the ability to evaluate one’s knowledge gaps in order to formulate learning plans ; therefoThis study is a retrospective mixed method analysis of secondary data collected by the Family Medicine Residency program at the University of Manitoba. Upon residents’ completion of training, the program gathers all educational documents generated in each resident’s file for collation by the University. The FNs collected for use in this study were copied from these folders prior to being transferred to the university files.FNs were de-identified prior to the start of this study by administrative personnel from the Department of Family Medicine. All study procedures were conducted in accordance with relevant guidelines and regulations once a review was completed and approval received from the University of Manitoba Health Research Ethics Board # HS22239 (H2018:402). Informed consent was obtained from all participants except for graduates who had already completed the training program, in which case de-identified data was available from a department database for analysis. Guidance regarding access and use of this dataset was provided by the Privacy Officer of the University of Manitoba.The FNs analyzed in this study were generated by a random sample of 16 residents from among the 70 residents (who will be included in the overarching study) who completed their training in June 2018, yielding 520 field notes for analysis. The University of Manitoba’s Family Medicine residency program includes residents from a variety of streams broadly categorized as the Urban, Rural and Northern Remote streams. While residents from all streams receive well-rounded educational experiences required to become confident generalists, how they learn this material and the settings in which they practice differ. Urban stream residents complete the majority of their training in Winnipeg, while Rural steam residents train in diverse communities ranging in size from 1000 to 40,000 people , 14. ThiThe FNs were analyzed using basic descriptive statistics (frequencies and means) for various domains including FNs generated, patient populations served, CanMEDS-FM roles demonstrated, and the CFPC Priority Topics  discusseA qualitative content analysis  of the wAnalysis of the data demonstrated a wide variation in the total number of FNs generated by the residents during the course of their two-year residency program (range = 1–123). An average of 32.5 FNs was found for all 16 residents with a notable difference in the average numbers of FNs among residents in the Urban versus Rural streams (15 and 50 respectively). Of note, there was one Rural FN “super-user” (resident V1616) who generated roughly 31% of the FNs documented by all Rural residents; removing that resident from the data set decreased the Rural stream average to 39.6 FNs. There was also cyclical variability in the number of FNs generated over a two-year period in both Urban and Rural streams which aligned temporally, however peaks in FN generation were more pronounced in the Rural stream , the most common documented Domains of Care were Care of Adults  followed by Care of the Elderly  and FM Expert- Clinical Reasoning  primarily addressed the domains of history-taking (26%), physical exam skills (25%), and management (25%), whereas constructive comments (“Would Do Differently”) addressed management (26%) and physical exam skills (18%) as well as general knowledge and skills  (33%). The action plans were analyzed in terms of learning domains, where feedback was directed toward a need to increase knowledge (64%) and improve skills (32%), with very few recommending changes in attitude (3.4%). A thematic content analysis of these sections within the FNs can be described in terms of four emergent themes that describe residents’ performance: Patient-Centered Care, Patient Safety, Achieving Balance, and Confidence Within each of these themes, comments reinforcing positive aspects of residents’ performances and remarks on areas of improvement were identified . In contrast, feedback for improvement addressed residents’ skill and confidence in conducting difficult conversations and exploring patients’ illness experiences in greater depth .The theme of Patient Safety describes how residents recognized issues where patient safety was at risk and how the need to ensure patient safety impacted residents’ assessments, clinical reasoning, and decision-making . In contrast, comments for improvement identified situations where residents needed to think more broadly in order to ensure nothing dangerous was overlooked ” [G1010]).The theme of Achieving Balance conveys situations where residents functioned independently while also recognizing their own limits through asking for assistance when needed, as well as times when they balanced both remaining professional while demonstrating empathy . Comments for improvement demonstrated how residents needed to strike a balance between being cautious and trusting their knowledge in order to collaborate with patients and other health care providers .The theme Confidence focuses on residents’ attitudes in their assessments, clinical reasoning, and decision-making and how they affected the next steps they took in patient care. In writing, “ruled out red flags allowing safe [discharge] w/out exact [diagnosis]” (Z1290) this participant demonstrated confidence in his assessment and reasoning. Similarly, “trust my clinical judgment (sic) on sickness of patient.” (A1278) demonstrated the need to be more confident in the accuracy of his/her assessment.Finally, the theme This study examined the content of FNs generated by a cohort of residents from Urban and Rural Family Medicine streams to gain a better understanding of the content of FNs and how residents reflected on their progress and future learning needs. It is evident from the findings that there is great variability in FN use between streams. In addition to considering the structural factors and dynamics that may influence FN use, we offer a theoretically informed interpretation of the results.Within this sample of residents, FN generation was three times greater in the Rural stream compared to their Urban counterparts. This may be due to the structural differences between the two training programs. The Urban stream and the majority of the Rural streams have set Family Medicine Block Time (FMBT) completed in Family Medicine teaching units with additional specialty-based rotations interspersed between the blocks , 14. HowThe overall uptake and content of FNs may be influenced by how users perceive the overarching purpose of FNs. Some residents may perceive FNs as a self-reflection tool for their performance for that specific day rather than a means to facilitate ongoing self-reflection. Furthermore, some residents may simply interpret FNs as a personal tool for single use, rather than as an instrument for informing comprehensive assessment. The same may be said for educators who may view FNs as a tool to foster discussion about a particular patient encounter rather than using the notes to chart residents’ progress over the course of their program. This “one-time” perspective risks impeding residents’ ongoing self-reflection and preceptors’ longitudinal assessment of residents’ development ; similarFNs enable the assessment of easily observed skills such as communication, procedural skills, and medical knowledge, while more latent, deep-seated skills such as professionalism and leadership may be more challenging to evaluate and as such may be overlooked. While FNs contain lists of CanMEDS-FM roles that can be marked as “done well” or “requires improvement”, it is essential that this assessment is supported by narrative feedback from both learners and preceptors , 5. In tInterviews with both residents and educators may aid in clarifying current perspectives on FN use and thus address these possible inconsistencies. Overall, the objective of FNs should be clearly expressed to both residents and educators in the future in order to maximize their efficacy as either learning or evaluative tools (or both) within the Department of Family Medicine. Additionally, residents should be encouraged to provide detailed self-reflection on their performance without fear of penalization. This will allow for optimization of the use of FNs as a tool to guide residents through areas of unfamiliarity and discomfort onto higher levels of learning.Within the FN documentation in this study, some CFPC Topics, CanMEDS-FM roles, and Domains of Care were recorded more frequently, while others were documented less frequently. The phenomenon of variable documentation of CanMEDS-FM roles is not unique to this study. Mathew, et al., found that the roles of Clinical Expert and Communicator were assessed most frequently, with Professional being assessed less commonly and Leader not being assessed at all . ConsideThe presented results indicating variable use and the tendency to address more simple issues suggest potential convergence in the concepts of comfort and building competence as possible influential factors shaping the content of FNs and residents’ motivations to share their challenges and learning goals. The idea that learners’ comfort and competence may influence educational advancement was presented by Vygotsky’ in his theory of learning. Vygotsky identified three learning zones: a central “zone of comfort” where learners feel comfortable in their knowledge and skills, the Zone of Proximal Development (ZPD) surrounding this where learners move into the unknown with support and mentoring to grow in confidence and competence, and the “zone of danger” (outside the ZPD) where fear and loss of control inhibit learning . By chooVygotsky’s theory may also be used as a framework to explore residents’ motivations for reflection upon certain topics and to hypothesize how residents’ self-image and comfort may impact their educational advancement. During Family Medicine residency, residents must master a vast amount of knowledge and skills in a short period of time. They must not only acquire this knowledge, but also confidently apply it in everyday practice when making medical decisions. Despite being at an early stage in their professional career, residents are expected by patients and educators alike to demonstrate relatively high degrees of certainty as practitioners while simultaneously navigating their roles as learners. As such, there may be an internal struggle for residents as they balance the roles of both physician and learner –23. This, areas they may be more familiar assessing, more often than they discussed their ability to strike a balance between practicing independently and recognizing limits in their own capabilities. This may indicate a preference in reflecting upon more overt skills that were likely previously assessed by supervisors rather than assessing their leadership skills, a role they may not completely identify with at this point in their professional development.Throughout this study, this phenomenon appears to be evident in that certain competencies, particularly those addressing complex psychosocial issues, were selected less frequently than other competencies. For example, of the 99 CFPC Priority Topics, none of the FNs documented the following topics: Domestic Violence, Grief, Infertility, Lifestyle, Obesity, Parkinsonism, Rape/Sexual Assault and Somatization. Additionally, the CanMEDS-FM roles of Communicator and Clinical Expert have been assessed more commonly than the Professional and Leader roles. Another example of how this theory has manifested is that in the narrative portions of FNs, residents reflected upon their communication and clinical reasoning skillsThese early findings may indicate increased comfort in reflecting upon areas of familiarity while avoiding topics that are out of residents’ comfort zones. As such, they may feel reluctant to reflect upon new experiences considering these are areas where they may feel less confident. With the breadth of knowledge and skills generalists are required to obtain, residents may doubt their abilities at such an early stage in their careers . If learAvoidance of unfamiliar or difficult topics and domains may impede honest self-reflection and residents’ overall development into well-rounded and confident attending physicians. Educators must aim to foster a safe and encouraging learning environment that optimizes education while providing safe patient care. Only within a supportive training environment will learners feel they can honestly evaluate and debrief their performance with preceptors without fear of judgement or negative evaluation. It is imperative to encourage collaboration between residents and preceptors in pushing the boundaries of residents’ comfort zones in order to learn and reflect on the necessary skills, both in knowledge and application, required to become competent and confident generalists.This project analyzed a subset of FN data from an overarching study. In the overall project, the resident sample will be from the Urban, Rural, and Northern Remote streams whereas this inquiry only analyzed data from Urban and Rural residents. This limits the generalizability of the findings. Secondly, one participant within the Rural stream generated a significant number of FNs, which may have skewed the data from that stream, while simultaneously highlighting differences in FN uptake. Thirdly, residents typically only generate one FN per day regardless of the number of patient encounters, thus the data may not reflect the variety of patient care experienced in day-to-day practice reflecting only what residents choose to document. Finally, factors influencing residents’ selection of FN topics were not captured by the study design.Differences in FN generation were found between the Urban and Rural streams, while both streams demonstrated cyclical variation over the course of the two-year residency. More in-depth analysis of FN content revealed particular medical encounters and CanMEDS-FM competencies were more commonly reflected upon, while residents did not frequently create FNs which addressed challenging psychosocial topics or marginalized or vulnerable populations. Vygotsky’s work on learners’ attainment of competence is proposed to explain why learners did not explore challenging or unfamiliar topics. By remaining close to the zone of comfort, learners miss valuable learning opportunities, impeding their professional development.The responsibility of providing patient care during residency training is difficult to balance with one’s self-assessed knowledge deficits or weaknesses, especially considering the breadth of knowledge and skills required for generalist care. It is essential that feedback regarding the perceived use and effectiveness of FNs in the Family Medicine residency program is obtained and considers discomfort or unfamiliarity with certain topics, within a safe environment, to foster growing confidence and competence.Further research is needed to explore why some residents and preceptors become “super-users” of FNs compared to others, and how the learning environment may impact the completion of FNs by residents. As such, both quantitative and qualitative research, along with discussions with both learners and educators alike, should be undertaken to fill in these knowledge gaps in order to better support Family Medicine residents throughout their training. Overall, it is essential for programs to be cognizant of how the learning environment can influence residents’ engagement in self-reflection. It is vital that programs and residents collaboratively take steps to address any obstacles to honest self-assessment and acquisition of constructive feedback in order to ensure the competence of residents by the end of the program.


In 20% to 30% of patients with follicular lymphoma (FL), the disease shows an aggressive behavior.Novel biomarkers are available in FL each with a different ability to identify high-risk patients.Further improvement in the management of FL will likely be achieved by means of risk adapted therapies.For many years, risk in follicular lymphoma (FL) has been defined with conventional clinical prognostic factors and indexes with the follicular lymphoma international prognostic indexes (FLIPI and FLIPI2) being the most frequently used scores.Recently, Casulo et alPOD24 is an important step toward a better understanding of FL; however, patients would rather benefit from a better risk stratification closer to FL diagnosis, thereby allowing the development of risk-modifying approaches. In that respect, the heterogeneity of high-risk FL which is so far defined by refractoriness and transformation needs to be better understood. These patients’ higher risk of dying is mainly caused by lymphomaA number of studies have found associations between several pathologic features such as histologic grading, proliferation index, and microenvironment in diagnostic FL biopsies and varying degrees of disease aggressiveness, but have not confirmed these features as reliable prognosticators in the era of immunochemotherapy.Two attempts have been made to integrate clinical prognostic factors with molecular biomarkers: Pastore et al18F-fluordesoxyglucose (FDG) avidity was confirmed in the majority of FL, the prognostic value of quantitative parameters obtained from baseline FDG-PET/computed tomography has been analyzed. Of these parameters, standardized uptake value (SUV) has been shown to be a good tool to identify areas at higher risk of histologic transformation and could thus be used to guide diagnostic biopsies. More importantly, in a recent study by Meignan et al,Finally, since The above-mentioned molecular and metabolic biomarkers represent new tools to identify high-risk patients at diagnosis and might be used to support biology guided therapies . However, they both show limitations in their reproducibility and require further investigations in the context of prospective studies and in different subgroups of FL patients .Response to therapy assessed either with FDG-PET or with highly sensitive molecular techniques that are able to measure cell-free DNA or to determine low levels of the t chromosomal translocation  have recently been suggested as useful prognostic tools.,,,In summary, several biomarkers and prognostic factors are currently available to identify a subgroup of approximately 20% to 30% of patients with FL whose lymphoma show an aggressive clinical behavior. The use of novel techniques to measure cell-free or tumor-free DNA holds promises to a deeper understanding of FL heterogeneity, and for a better monitoring of response to treatment, hopefully leading to the identification of novel biomarkers.


Adult spinal deformity (ASD) is a complex and heterogeneous disease that can severely impact patients’ lives. While it is clear that surgical correction can achieve significant improvement of spinopelvic parameters and quality of life measures in adults with spinal deformity, there remains a high risk of complication associated with surgical approaches to adult deformity. Over the past decade, utilization of surgical correction for ASD has increased dramatically as deformity correction techniques have become more refined and widely adopted. Along with this increase in surgical utilization, there has been a massive undertaking by spine surgeons to develop more robust models to predict postoperative outcomes in an effort to mitigate the relatively high complication rates. A large part of this revolution within spine surgery has been the gradual adoption of predictive analytics harnessing artificial intelligence through the use of machine learning algorithms. The development of predictive models to accurately prognosticate patient outcomes following ASD surgery represents a dramatic improvement over prior statistical models which are better suited for finding associations between variables than for their predictive utility. Machine learning models, which offer the ability to make more accurate and reproducible predictions, provide surgeons with a wide array of practical applications from augmenting clinical decision making to more wide-spread public health implications. The inclusion of these advanced computational techniques in spine practices will be paramount for improving the care of patients, by empowering both patients and surgeons to more specifically tailor clinical decisions to address individual health profiles and needs. Over the past couple decades, our knowledge of adult spinal deformity (ASD) as a complex disease has increased immensely. It is now well established that ASD is a heterogeneous entity that can cause significant pain and disability in patients, with worse deformity exacerbating these symptoms –4. As ouDue to the extensive variability of ASD presentation and the many factors pertinent to patients’ outcomes, ASD offers a unique opportunity for the application of advanced analytics in spine surgery. Throughout the history of operative and nonoperative management for ASD, spine surgeons have relied on their clinical judgment and large, retrospective studies to better inform decision making and to counsel patients regarding their treatment plan. Often times, the personal experience of surgeons provided the information, and this was heavily dependent on the surgical volume and exposure to ASD cases at various spine centers. While relatively rare in the spine literature, early predictive models helped us decipher some of the subtleties of spine surgery outcomes, with even fewer focusing on surgical correction of ASD and its associated risks and outcomes –29. HoweAs we transition into an era of medicine largely influenced by the digitization of data through the incorporation of electronic medical records, we have gained access to an astounding amount of patient information that can be used to create more robust and complex analytics. In conjunction with this explosive growth in available medical data, our ability to process this information through refined computational algorithms has progressed as well. Over the past few years alone, we have seen various disciplines within medicine gradually adopt artificial intelligence (AI) techniques primarily through the use of machine learning methods to process and analyze unprecedented amounts of data. Within neurosurgery specifically, several groups have taken significant strides towards implementing artificial intelligence into clinical practice. Titano et al. showed at a prominent academic center in New York that a machine learning framework utilizing a 3D convolutional neural network (CNN) could successfully triage radiology studies to help monitor for acute neurologic events . The algGiven the prevalence of surgical utilization for ASD in the global spine community, it is imperative to first understand the true impact of this disease. As the number of ASD surgical cases continues to increase exponentially with respect to total volume as well as rate per 100,000 adults, there is concern among both physicians and healthcare payers regarding the reported rates of complications and the burgeoning cost of treatment . To bettp < 0.01). In addition to the large increase in new patients undergoing ASD surgery, there was a sustained reduction in both major and minor complications observed at 90 days , one year  and two years of follow-up . Along with the reduction in complication rates observed over the past decade, the combined dataset also demonstrated a significant decrease in two-year reintervention rate  as well as surgical invasiveness as defined by number of fused segments , patients undergoing pelvic fixation , and patients undergoing three-column osteotomies (3CO) . It is important to consider that these trends pertain specifically to high-volume spine deformity centers, and that decreasing invasiveness along with less pelvic fixation and osteotomies is a concurrent observation, rather than a causative relationship due to increased ASD literature and surgeon experience. Notably, this decrease in patient morbidity was also accompanied by an improvement in patient HRQOL scores  in addition to degree of sagittal correction as measured by pelvic incidence-lumbar lordosis (PI-LL) mismatch  [Of the 2286 patients included in the combined dataset, a total of 1151 patients operated on at 17 different sites met inclusion criteria. While baseline characteristics of patients including age, HRQOL scores, sagittal imbalance and ASA grade did not change from 2010 to 2016, there was a significant increase in overall patient recruitment  . In summTo effectively utilize machine learning for predictive modeling, it is critical to understand the concepts and methods behind their implementation. At is core, AI represents the creation of a system that mirrors our innate ability to process information and dynamically learn as we are exposed to new situations. As it attempts to recapitulate our natural intelligence, AI makes use of numerous computational techniques, most commonly machine learning, which is considered a subset of AI. One of the core principles of machine learning is the idea of “training” algorithms on data that is available, and allowing the algorithm to determine mathematical relationships between variables inherent in the data. By removing the process of manually coding or interrogating relationships between selected variables, machine learning eliminates user bias regarding which variables are relevant or not for the desired analysis—often relationships that are not intuitively apparent can be identified by these methods. Traditional statistics including linear/logistic regression is hypothesis driven, and as such relies on many assumptions that are often not generalizable. Hypothesis-generated studies inherently require selection of predictor variables, which limits factor inclusion and can lead to omitted variable bias due to possible confounders being missed. Conversely, machine learning allows for the wide-spread inclusion of input variables and relies on robust algorithms to determine correlative relationships within the data. The power of machine learning techniques becomes readily evident in the context of ASD surgery, where patients often embody highly variable symptoms and medical profiles. Once algorithms are trained on available data, they are then “tested” on separate test sets, to evaluate the accuracy and performance of the constructed model. The test set gives the user an idea of how well the model will perform when deployed prospectively on novel data. Generally, data acquired for predictive model generation is split 80:20 or 70:30 into training and testing sets, respectively. Model training itself then tends to follow an iterative process, in which various models are tested for efficacy using a technique called cross-validation. In cross-validation, the training data is repeatedly partitioned in a random manner, such that in each iteration a portion of the actual training data is cordoned off as a “validation set”, to serve a similar purpose to the test set and allow for parameter tuning and model optimization. A summary of this process is depicted in Fig. While it is important to recognize that statistical models still serve a vital role in outcomes research, there are several distinguishing factors regarding their applications when compared to machine learning predictive models. First and foremost, statistical models exist to characterize the relationship between data and an outcome variable, allowing us to infer the relationships between variables and test different hypotheses. Machine learning on the other hand is a computationally intensive technique that derives its utility from being able to process extraordinarily large amounts of data spanning diverse and heterogenous variables to make highly accurate and repeatable predictions. What statistical models lack in predictive ability however, they make up for in ease of interpretability. Predictive models created by machine learning algorithms offer far more powerful predictive capabilities, but as a result are often more difficult to interpret given their complexity.Despite their seemingly limitless potential, there are several components integral to the proper development of predictive models. One of the most essential requirements is having access to robust data. Having a large amount of data is in itself not sufficient for applying machine learning methods. It is essential that the data be high-quality as well, such that a smaller matrix of reliable and high-quality data will be more useful than a larger matrix of outdated or inaccurate data. The quality of the data can be reflected in its consistency , accuracy (numbers accurately reflect the given attribute without typos or mistaken entries), completeness , and the absence of duplicate or corrupted data entries. Sparsely populated data or overly complex models can also result in a phenomenon termed “overfitting”, where a machine learning model specifically caters too closely to the data it was trained on, and as a result loses accuracy when being applied to novel data. These issues can be mitigated by acquiring larger or higher quality datasets, as well as with diligent optimization of model parameters and adherence to principles such as cross-validation and strict training/testing as described earlier. Other techniques to avoid overfitting include using ensemble methods, which are machine learning methods that combine predictions from several different models to optimize the overall predictive ability, and bootstrapping. Bootstrapping is a statistical technique that involves sampling with replacement from a dataset, to estimate parameters about the entire dataset/population. Bootstrapping can be performed over several iterations, and the benefit of sampling with replacement is that some data entries may be considered zero, once, or more times, and thus expected variance is lowered as each bootstrapping iteration will be independent from its peers. Ensemble methods include ‘bagging’ , which involves training multiple, complex models in parallel using bootstrapped samples and then averaging the responses of each of the models, and ‘boosting’ which trains simpler models in sequence using the entire training set, such that each subsequent model builds upon and learns from the failures of its predecessor . In contrast, statistical models still allow the user to make generalizable inferences using relatively small amounts of data. While observational studies can suggest average outcomes from specific interventions across entire populations, it is impossible to conduct accurate comparisons between observed outcomes in a specific patient, and hypothetical outcomes that may have risen from alternative management strategies with simple statistical models. For ASD surgery in particular, this is where predictive models derived by machine learning can have a significant impact—given the large spectrum encompassed by ASD patients, the incorporation of machine learning algorithms into predictive analytics can offer unprecedented prognostic information to augment the decision making of surgeons and bolster their ability to counsel patients. This granularity can help tailor treatment regimens to a patients’ specific needs, helping deliver a more personalized form of healthcare. In this current era of widely accessible computational programs, it will be imperative that physicians and researchers keep in mind these fundamental principles when reviewing studies in the literature, and meticulously follow the appropriate steps of model generation to avoid sharing misleading results.To help quantify and stratify the significant heterogeneity in the clinical presentation of ASD patients before predictive analytics, metrics such as the ASD frailty index (ASD-FI) were developed . The conFollowing the successful inclusion of the ASD-FI for evaluating thoracolumbar deformity patients, similar methods were subsequently applied for cervical deformity. As a result, the cervical deformity (CD) frailty index (CD-FI) was developed and subsequently modified for ease of implementation as the modified CD-FI (mCD-FI) , 45. SimThe adoption of metrics such as the frailty indices used for both ASD and CD represent a significant paradigm in the generation of predictive models. The utilization of frailty indices demonstrate how more traditional statistical methods can still be used to elucidate drivers of postoperative outcomes, and why hypothesis-driven studies still serve a critically important function in clinical studies. However, despite the important correlative analysis exhibited by the use of novel frailty indices, the final outcome of an odds ratio has limited applicability to individual patient cases, instead representing a general correlation across a broad population. The impetus behind these research efforts is to ultimately create better systems for prognosticating patient outcomes. By identifying an important correlative factor and its constituent features, the frailty index studies highlighted several important variables which can then be included in machine learning predictive models to better prognosticate patient outcomes. To do so will require synergy between the development of novel metrics such as the frailty indices to better characterize patient profiles, and rigorously constructed predictive models, that can utilize this information. This combination of statistical methods and machine learning algorithms will serve to enhance patient counseling during clinic visits and bolster the armamentarium of spine surgeons.To date, spine surgeons have already begun to make significant strides in the creation of more complex predictive models through the implementation of machine learning techniques. The most common methods currently being employed focus on the use of decision tree-based learning models. In general, these algorithms utilize the creation of classification or regression trees to predict a desired variable such as complication risk or a specific outcome. In generating these predictive models, a variety of variables are incorporated as input features for model training. These variables can include patient demographic information, comorbidities, comprehensive indices such as the Charlson Comoborbidity index (CCI) and FI, radiographic parameters, surgical characteristics, HRQOL scores and intraoperative information. Different techniques such as bootstrapping or ensemble methods have also been judiciously used to combine several different (and possibly weaker) algorithms into a single, stronger classifier, to minimize overfitting while offering improved predictive value. These predictive analytics have been widely applied across the spectrum of ASD surgery, including prediction of intraoperative , periopeWhile most applications of predictive models focus on determination of postoperative outcomes, Durand et al. developed a predictive model for intra- and postoperative blood transfusion requirements with a cohort of 1,029 ASD patients. Using an 80:20 split for training and test sets, their final decision tree and random forest models predicted transfusion rates among ASD patients with area under the curve (AUCs) of 0.79 and 0.85, respectively . The ranBuilding on the success of the earlier described applications, predictive analytics have also been deployed for assessment of a variety of postoperative outcomes including proximal junctional failure (PJF) and proximal junctional kyphosis (PJK) , 50, pseThe final domain of ASD surgery that has seen significant advancement in its use of predictive analytics has been regarding HRQOL outcomes for ASD patients following surgical correction –56. ThisWhile each of the studies described above represents significant forays into the wide-spread adoption of predictive analytics for ASD surgery, there are still improvements to be made in both methodology and applicability. Many of the early predictive models use relatively simple machine learning methods like decision trees, which can have a high propensity for overfitting their models. In addition, a large portion of these studies are limited by their sample size, which is a larger problem that exists within spine surgery. The careful maintenance and construction of robust databases is resource intensive and mandates collaboration across diverse institutions and spine centers to achieve greater sample sizes. In medicine, we are often presented with class imbalance problems when trying to develop predictive models using machine learning. Class imbalance is a phenomenon common in medical datasets, where one class or outcome can represent the majority of data, while a different class or outcome represents a significant minority. As a result of this disparity, predictive models trained on imbalanced data can be heavily biased towards the majority outcome, providing high AUC, accuracy and sensitivity, but unemployable sensitivity when predicting outcomes/events with low incidence. Techniques such as cost-sensitive learning, employment of alternative (more complex) algorithms, and under/oversampling the majority and minority classes respectively, can help mitigate possible imbalance. Taking these shortcomings into consideration, we will next explore a few landmark studies that utilized additional, higher quality methodologies and datasets to create even more robust predictive analytics.2. MAE values across the selected models ranged from 8 to 15%, indicating successful model fitting and highly accurate predictive capabilities [Significant efforts have been undertaken by the ISSG and ESSG in publishing pioneering studies in the field of predictive analytics for ASD surgery. Through an immense collaborative venture spanning multiple countries, spine centers, and numerous surgeons, the ISSG and ESSG have curated a high-quality (as described earlier) and comprehensive database of ASD patients through which they have developed groundbreaking complex analytics. To substantiate the earlier pilot studies demonstrating the feasibility of using predictive models for HRQOL metrics, Ames et al. published what is currently the most expansive study on predicting patient reported outcomes (PROs) . In theibilities . A signibilities . The sigBuilding upon the earlier predictive analytics for postoperative outcomes and complication risk following ASD surgery, the ISSG and ESSG similarly developed rigorous and more technically complex predictive models using their expansive ASD datasets to enhance predictive capabilities . The relR2 measures ranging from 56 to 57% for 90-day direct cost, and 29–35% for 2-year direct cost prediction. In addition, the generalized linear regression models used by the authors with forward stepwise selection were able to explain 81% and 64% of the variance in direct cost at 90-days and 2-years, respectively. While these metrics may reflect relatively lower predictive accuracies compared to other simpler models, their design allowed for easier interpretation of model results, and importantly the authors were able to identify variables such as number of levels fused, surgical approach, use of interbody fusion, length of hospital stay, and the attending surgeon as the top predictors of both direct cost, and catastrophic cost as well. The identification of patients who may be at risk of incurring catastrophic costs following ASD surgery may help healthcare initiatives to bundle payments for high-impact and resource intensive treatments like ASD surgery, as well as provide surgeons and hospitals with insight into means of cost reduction in ASD surgery.Moving beyond predicting complications and outcomes, Ames et al. additionally applied similar methodology to predict patients who may experience catastrophic costs following surgical correction of ASD at 90-day and 2-year time points to better understand the economic impact of ASD surgery . ThroughLastly, in the most advanced use of machine learning and AI for ASD surgery to date, Ames et al. published for the first time the use of powerful, unsupervised learning algorithms to develop a novel classification system for ASD patients . In thisNow that the groundwork has been laid for developing predictive analytics in ASD surgery, it is important to consider more diverse applications of machine learning techniques to push the field further. The computational power encapsulated by these algorithms can provide remarkable insight into numerous facets of ASD surgery. One such application is the use of predictive models for establishing performance benchmarking in spine centers. Benchmarking is critical to the continual refinement of the ASD surgical treatment plan, as it allows institutions to assess their ability to effectively treat a disease, and subsequently identify areas of improvement. Previously, benchmarking was conducted by assessing rates of various outcomes across many different sites and institutions, and then determining an average rate across this extremely diverse cohort. This however is not an accurate assessment, as there are many nuanced factors that can contribute to the performance of an institution, and as such complication rates and outcomes can vary significantly across centers. Some of these differences could relate to case volume and surgeon experience, patient complexity, as well as institutional support staff and operating room protocols, among many others. To remedy this heterogeneity, the ISSG conducted a pilot study using previously published predictive models for pseudarthrosis  and PFJ/In addition to its performance benchmarking efforts, the ISSG has also explored the utility of applying predictive models for conducting ASD studies at the population level. The motivation behind this study was to better understand how surgical utilization for ASD could be better optimized given the enormous financial burden associated with ASD surgical management. The healthcare costs of first-world economies are accelerating at an unsustainable rate, and better-informed patient selection can help with resource allocation to maximize patient benefits while also relieving some of the associated direct costs of complex disease treatment. To perform this study, the ISSG used previously established predictive analytics for preoperatively determining rates of MCID and complication risk for patients to assess their feasibility for simulating population-level health data. A total of 1245 prospectively collected patients treated at 17 different ASD centers were pooled for this analysis, and clinical outcomes of MCID, complication, and reoperation rates were predicted using gradient boosting classification. Patients were then stratified in increments of 10%, based on their predicted MCID and complication rates. Creating increments of 10% in both of our outcome variables allowed us to comprehensively profile the risk-to-benefit of surgery, and understand the financial implications at each of the thresholds. To determine a cohort of optimal surgical candidates, we considered the sub-group of patients with predicted MCID rates > 50% and complication rate < 20% as a hypothetical simulation, to model the population likely to benefit most from surgical correction (Fig. Predictive models and the implementation of machine learning techniques to augment surgeon decision making have substantial potential to improve surgical outcomes for patients with ASD. While significant progress has been undertaken by spine surgeons, there remain several challenges and important obstacles that must be overcome before the wide-spread adoption of predictive analytics in spine practices. The most successful predictive models have been created in the context of a deep understanding of the problem being studied and the underlying data-generating process. As such, it will be imperative moving forward that surgeons across institutions and even across countries collaborate to generate large, comprehensive, and robust databases such that machine learning techniques can be properly utilized. Additionally, there will be a need for consolidation of the various published models, such that surgeons can begin to integrate them into their practices. We hope to see the wide-spread availability and adoption of consolidated calculators and predictive models in the near future, as the ASD calculator developed by the senior authors is currently undergoing alpha testing at various ISSG/ESSG sites. While extremely powerful, great care must also be taken during model development and analytics must be meticulously built using the best practices of machine learning theory. Predictive models only offer partial solutions and must continue to be complemented with rigorous hypothesis testing to truly understand the causal effects in surgery, with further prospective validation for assessment of model performance. This task is certainly complex; however, the challenge ahead should not dissuade surgeons and researchers from continuing to generate predictive and explanatory models. The ability to leverage powerful computational techniques will propel spine surgeons confidently into the era of personalized medicine, allowing them to complement decades of clinical experience and practice with predictive models and insightful data to meaningfully enhance patient care.


A diverse range of platforms has been established to increase the efficiency and speed of clinical trials for Alzheimer’s disease (AD). These platforms enable parallel assessment of multiple therapeutics, treatment regimens, or participant groups; use uniform protocols and outcome measures; and may allow treatment arms to be added or dropped based on interim analyses of outcomes. The EU/US CTAD Task Force discussed the lessons learned from the Dominantly Inherited Alzheimer’s Network Trials Unit (DIAN-TU) platform trial and the challenges addressed by other platform trials that have launched or are in the planning stages. The landscape of clinical trial platforms in the AD space includes those testing experimental therapies such as DIAN-TU, platforms designed to test multidomain interventions, and those designed to streamline trial recruitment by building trial-ready cohorts. The heterogeneity of the AD patient population, AD drugs, treatment regimens, and analytical methods complicates the design and execution of platform trials, yet Task Force members concluded that platform trials are essential to advance the search for effective AD treatments, including combination therapies. Disappointing clinical trial results continue to mount in the search for effective treatments for Alzheimer’s disease (AD) despite increasing knowledge about underlying mechanisms and potential therapeutic targets  are public-private partnerships created to facilitate the development of AD therapeutics and advance scientific understanding of the optimal ways to prevent and treat AD. To accomplish these objectives, DIAN enrolled participants from around the world with dominantly inherited AD (DIAD) mutations that confer nearly 100 percent certainty of developing AD. By longitudinally monitoring both symptomatic and presymptomatic participants with clinical assessments and physiologic and pathologic biomarkers, DIAN established the progression of biomarkers across the continuum of the disease and demonstrated a biomarker profile associated with presymptomatic disease . This prBateman, Randall J., Chengjie Xiong, Tammie L.S. Benzinger, Anne M. Fagan, Alison Goate, Nick C. Fox, Daniel S. Marcus, et al. “Clinical and Biomarker Changes in Dominantly Inherited Alzheimer’s Disease.” New England Journal of Medicine 367, no. 9 : 795–804. 10.1056/NEJMoa1202753. Reprinted with permission.DIAN-TU built a flexible and robust platform based on observational data from DIAN-Obs along with disease progression models, comprehensive biomarker studies, input from participants and family members, and inclusive discussions with stakeholders. The platform enabled the design and execution of prevention studies that can accommodate a range of prevention and treatment trials. Since the rarity of these mutations limits the number of trial participants, DIAN-TU has established study sites around the globe: in North and South American, Europe, Japan, Korea, and Australia, with additional sites planned in China and other countries.testing multiple drugs and targets in paralleloptimizing the trial design in view of the limited number of participantsusing a pooled placebo and control group, building up data over time to accumulate power to detect a drug effectevaluating the magnitude of target engagement based on each drug’s mechanism of actionadapting the trial in response to biomarker findings, magnitude of drug effects at different doses, and safety signalsincorporating novel biomarkersapplying learnings from each study into future studies in the platform.The DIAN-TU Trial Platform is designed to improve the efficiency of trials by:The DIAN-TU platform launched in 2012 as a 4-year secondary prevention trial in participants with DIAD mutations and expected onset ranging from −15 to +10 years and Clinical Dementia Rating (CDR) score of 0 , 0.5 (very mild cognitive impairment), or 1 (mild cognitive impairment consistent with early dementia) . Nearly As the two-drug trial continued, adaptations to the platform were made. Planned as a two-year biomarker trial, the study transitioned to a four-year cognitive endpoint trial after the biomarker analysis confirmed that the drugs were engaging their targets. Part way through the trial, a decision was made to increase the dose of both drugs based on external data.Other components were also added to the platform. These included a run-in period during which data from cognitive measures and tau PET studies were collected in preparation for launching three tau next generation (NexGen) drug arms. Meanwhile, a primary prevention trial in DIAD participants 10 years before symptom onset is in the launch preparation stage. This trial will use a beta amyloid (Aβ)-targeting drug with the aim of preventing the development of plaques before they start.Topline results for the solanezumab/gantenerumab trial, reported in early 2020, showed that both drugs failed to meet the prespecified primary cognitive endpoint . NonetheThese analyses suggest two possible reasons that may explain the failure to detect a significant cognitive difference between groups: 1) although the dose was increased part way through the trial, most participants had received lower doses; by the time they were switched to the high dose, many symptomatic participants had already declined substantially, thus limiting the ability to evaluate the effect of the high dose; 2) some asymptomatic trial participants failed to decline and in some cases improved; thus it was not possible to detect a drug effect in those participants.The continuing data analysis also suggested that gantenerumab showed significant biologic effects on downstream biomarkers, suggesting that higher doses and longer treatment periods may be needed, especially for asymptomatic mutation carriers. While substantially advancing the field, these results were not able to definitively test the hypothesis. Nonetheless, gantenerumab is being studied in an ongoing exploratory open label extension .Other lessons learned from the trial include the need to enrich the study for participants likely to show signs of cognitive decline during the treatment period. The optimal approach to this challenge is not clear, but further analysis of biomarker data from this trial may provide some insight. In addition, other research suggests that some novel biomarkers or combinations of markers may be predictive of incipient cognitive decline. How they would be implemented in screening for trials, however, would present additional complexity. Researchers also continue to search for more sensitive cognitive assessments and tools that minimize practice effects, which could also potentially improve the ability to detect a treatment effect. Yet even if a test is developed to detect subtle cognitive change, the ability of that test to be applied to an AD trial could be limited if the cognitive change results from non-AD aging effects.The AHEAD Study is a platform-based study comprising two Phase 3 secondary prevention trials — the A45 trial and the A3 trial. Both four-year trials are testing Eisai’s anti-Aβ monoclonal antibody lecanemab (BAN 2401) but use a different dosing regimen depending on baseline amyloid burden. Together they are evaluating the efficacy of the treatment across the continuum of early-to-late preclinical AD as determined by positron emission tomography (PET) amyloid assessment. Using different radioligands, PET imaging enables the visualization of both amyloid plaques and tau neurofibrillary tangles. The AHEAD trials build on the findings of the Harvard Aging Brain Study (HABS) showing that early cortical amyloid accumulation is associated with subsequent tau accumulation in the inferior temporal neocortex, which is associated with neurodegeneration and cognitive decline . This stThe A3 trial aims to capture people with a measurable amyloid PET signal that is below a conventional threshold of amyloidosis. These participants with “intermediate” levels of amyloid  will be randomized to receive the drug or placebo monthly. Amyloid PET will be used as the primary outcome measure to determine whether lecanemab slows amyloid accumulation. Tau PET will be a key secondary outcome and cognitive testing will be an exploratory outcome.In the A45 study, participants with elevated amyloid will be randomized to receive the drug or placebo biweekly for the first two years followed by monthly maintenance dosing, with cognitive testing as the primary outcome. Amyloid and tau PET will be key secondary outcomes, and biomarker studies will also be an essential component of the trial along with additional cognitive and participant-reported outcomes.The first participants in AHEAD were screened in July and randomized in September. Worldwide, 100 sites have been identified. The AHEAD platform will use a common screening protocol for the two trials, 18F NAV4694 amyloid and 18F MK6240 tau PET at baseline, 2 and 4 years, and will enroll participants between the ages of 55 and 80. Participants under age 65 will need to have one additional risk factor . In response to the COVID-19 pandemic, the study team also plans to implement home infusion and potentially, supervised remote assessments.Other platforms are currently being planned to evaluate the efficacy of anti-tau drugs. While anti-tau therapies have gained support in the Alzheimer’s community because tau is thought responsible for neurodegeneration and cognitive impairment and because of the disappointing results of so many anti-amyloid therapies, many challenges remain due to the complexity of tau biology and the incomplete understanding of the relationship between tau and Aβ , 14. MorEli Lilly and Company has proposed using a master protocol, which should facilitate greater efficiency and accelerated drug development through the use of a common protocol, trial design, and infrastructure. Master protocols can be used in umbrella, basket, or adaptive platform trials to evaluate multiple therapies in the context of a single disease, a single therapy in the context of multiple diseases or disease subtypes, or multiple therapies in a single disease with arms added and/or terminated over time . For exaMaster protocols offer the opportunity for increased flexibility to explore multiple hypotheses and different drug combinations or patient populations. As with other platform trials they allow for shared placebo data, unbalanced randomization so that more participants are assigned to the active drug arm, and the potential for a lower screen failure rate. While master protocols may offer increased efficiency for testing multiple therapeutics in parallel, tradeoffs include increased complexity, reduced efficiency gains if fewer therapeutics enter as well as diminished flexibility compared to a single therapeutic trial. It is essential to consider the mechanistic differences among candidate tau therapeutics (and among immunotherapies targeting different epitopes) which may point to different selection criteria and outcome measures.To study treatments for tauopathies, a master protocol could be used to test multiple therapeutics in different tauopathies ; multiple therapeutics in one tauopathy at different stages of disease ; or multiple therapeutics in only one stage . Lilly’s consideration of a master protocol approach to testing multiple anti-tau assets could use one or a combination of these structures, although all of them present substantial operational costs and risks.A second tau platform protocol is being developed by the Alzheimer’s Clinical Trial Consortium (ACTC). Developers of the Alzheimer’s Tau Platform (ATP) envision a proof-of-concept platform to accelerate decision making in tau therapeutic development by simultaneously testing different anti-tau mechanisms in sporadic AD. They argue that a platform that enables testing multiple molecules and multiple regimens using factorial adaptive designs would improve the efficiency of recruitment, trial startup, and analyses. They also suggest that demonstrating efficacy may be possible more quickly with anti-tau versus anti-amyloid therapies since recent evidence suggest that tau PET agents can detect highly dynamic change in some individuals, e.g. those with higher levels of amyloid .DIAN-TU is also planning to add three anti-tau drug arms to its study in 2021. Since platforms can be especially helpful in assessing treatment response at different stages of disease, each arm will likely enroll both presymptomatic and symptomatic participants. Moreover, by testing different mechanisms and targets in the platform and using short-term biomarker studies, the platform approach may be able to get information on which targets are druggable at which stages of disease.The concept of platform trials is broad. DIAN-TU was designed to enable testing of multiple drugs using a shared placebo arm, with adaptive allocation based on randomization data. In contrast, the AHEAD 3–45 Platform provides benefits that are primarily operational rather than statistical comparisons between treatment regimens. Platform trials may also be used to explore multifaceted hypotheses, for example, to ask questions such as whether amyloid accumulation must be cleared before targeting tau. Different arms of a platform study can ask different questions simultaneously rather than sequentially to increase efficiency and accelerate drug development.Longitudinal biomarker and clinical studies in clearly defined cohorts (e.g. APOE homozygotes and heterozygotes) are needed to inform the design of platform programs. A matrix of outcomes  across the continuum of disease could eventually lead to a surrogate marker of efficacy, which could dramatically accelerate the search for effective therapies. For example, the European Prevention of Alzheimer’s Dementia (EPAD) platform began with a longitudinal cohort study to serve as a readiness cohort for proof-of-concept secondary prevention AD trials .Meanwhile, an international network of multidomain intervention clinical trials has been established to replicate the groundbreaking Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER), which demonstrated that a combination of lifestyle interventions reduces the risk of cognitive decline in older adults with vascular risk factors that increase their risk of dementia. The Worldwide FINGERS initiative (WW-FINGERS) provides a platform for investigators in different countries and cultures to adapt the FINGERS protocol while using similar protocols and sharing data .The Trial-Ready Cohort for Preclinical/prodromal Alzheimer’s Disease (TRC-PAD) project is a collaborative effort to establish an efficient mechanism for recruiting participants into very early stage Alzheimer’s disease trials , 21. CliThe heterogeneity of the AD patient population creates several complications in designing and executing platform trials. For example, the substantial clinical and biomarker heterogeneity in sporadic AD makes screening particularly challenging and increases the sample size needed to obtain adequate power. Regarding screening, accumulating data suggests that blood-based biomarkers have adequate sensitivity and specificity to be advantageous for selecting subjects with AD pathology although they are not yet supported for use as endpoints . IncorpoThe heterogeneity of the drugs themselves also increase both the potential benefits and the complexity of platform trials. Multi-arm platforms enable the parallel testing of different approaches and are particularly suited to testing combination therapies. However, the complexity of combination trials is decreased if one drug in a combination approach has been approved for a certain population. While there may be theoretical reasons for pursuing a combination of drugs that target both amyloid and tau, designing a regimen that allows for maximal biological engagement remains unclear, i.e., should amyloid be cleared or neutralized prior to targeting tau, or can targeting tau even in the presence of amyloid stop tau spread and downstream tau effects? Another issue with combination therapies is that two drugs with separate development plans may not be compatible from a logistical standpoint.Different modes of administration further complicate platform trials. Participants’ acceptance of a treatment may vary by mode of administration . Combining placebos when there are different modes of administration may not be feasible.Platform trials may employ different analytical methods to test hypotheses, address subgroup effects, and compare outcomes against control groups . InterimPlatform studies may also be challenging from a company perspective since they are expensive, complex, and may compete for resources with individual molecule programs. Collaboration of different company sponsors has the potential to share costs and risks, but presents other challenges including protection of intellectual property and data. Industry representatives acknowledged, however, that as data accumulate in a platform and the ability to use that data grows, care must be taken to ensure the data are appropriately used. The Task Force also recognized the importance of ensuring that the community of funders and sponsors understand the benefits and challenges associated with platform trials and that both public and private support are essential for this complex endeavor.Despite the challenges, many Task Force members believe that platform trials are essential to ensure that the field builds on and moves past the previous disappointments that have plagued amyloid trials. Moreover, they are necessary since conducting trials sequentially, drug by drug and severity stage by severity stage, takes much too long. Platform trials provide other opportunities that are essential for AD drug development by testing the predictive value of biomarker status and the surrogate value of biomarkers; evaluating individual drugs across multiple stages of disease; and accelerating the timing of testing combinations of drugs, which have historically waited until each drug in the combination has shown efficacy. Additionally, the Task Force advocated the importance of continued collaborations and partnerships to ensure that multiple investigators analyze multiple datasets, share data and knowledge, and learn from one another to gain insight into how to defeat this deadly disease.


Cortical network hyperexcitability is an inextricable feature of Alzheimer’s disease (AD) that also might accelerate its progression. Seizures are reported in 10–22% of patients with AD, and subclinical epileptiform abnormalities have been identified in 21–42% of patients with AD without seizures. Accurate identification of hyperexcitability and appropriate intervention to slow the compromise of cognitive functions of AD might open up a new approach to treatment. Based on the results of several studies, epileptiform discharges, especially those with specific features , frequent small sharp spikes (SSSs), temporal intermittent rhythmic delta activities (TIRDAs), and paroxysmal slow wave events (PSWEs) recorded in long-term scalp electroencephalogram (EEG) provide sufficient sensitivity and specificity in detecting cortical network hyperexcitability and epileptogenicity of AD. In addition, magnetoencephalogram (MEG), foramen ovale (FO) electrodes, and computational approaches help to find subclinical seizures that are invisible on scalp EEGs. We performed a comprehensive analysis of the aforementioned electrophysiological biomarkers of AD-related seizures. Brain rhythms are fundamental in maintaining normal cognition and behavior, and neuronal hyperexcitability has emerged as an important electrical abnormality that could not only lead to memory failure in the early stage of Alzheimer’s disease (AD) but also contribute to the progression of the disease . Once neThe presence of seizure and subclinical epileptiform activities have been shown to contribute to impaired memory and attention, especially cognitive fluctuation . A greatThe best-known biomarker of hyperexcitability in humans is the epileptiform discharge, which is defined as a paroxysmal EEG graphoelement (spike or sharp wave) with a duration of 20–200 ms that is clearly distinct from ongoing background EEG activity followed by slow waves  Figure . On scalEpileptiform discharges that had more robust morphological features including significantly larger trough voltage, peak-to-trough voltage, and slope of falling half-wave of the peak are more strongly associated with clinical seizures in AD . The guiThe detection rate of epileptic discharges in patients with AD by scalp EEG is significantly correlated with recording time. Approximately, 62% of patients with AD showed epileptic discharges in EEG recordings lasting 24 h  and onlyStudies in animal models with AD have established a direct link between neuronal hyperactivity and propagation of amyloid and tau pathology . Reyes-MFinally, there is growing evidence that scalp EEG epileptiform discharges in patients with AD have a variable association with clinical seizures . With anSmall sharp spikes (SSSs), also known as benign sporadic sleep spikes , are lowTemporal intermittent rhythmic delta activity (TIRDA) refers to the delta activity distributed in the temporal lobe with a frequency of 2.5–3.0 Hz and a sinusoidal or serrated waveform that occurs repeatedly and intermittently and has a strong association with mesial temporal lobe epilepsy (mTLE)  Figure . TIRDA oSlowing of scalp EEG activity has been observed in AD by studying early quantitative EEG power changes in patients with AD , and theInvestigating PSWE characteristics in aged mice, young 5xFAD mice, and young rats with epilepsy, Magnetoencephalography (MEG), which is thought to be more sensitive to discharges of tangential sulcal sources than EEG with a high temporal and spatial resolution , has alsImportantly, epileptiform discharges in patients with AD mainly occur during a sleep state, while the MEG recording cannot exceed a few hours at a time. Thus, it is nearly impossible to capture natural sleep or paroxysmal events during a sleep state by MEG. Considering the high requirement of equipment maintenance and the high costs of MEG examination, it is difficult to carry out MEG as a routine examination in general patients with AD. However, for patients with high suspicion of cortical hyperexcitability without visible epileptiform discharge on scalp EEG, MEG examination can be chosen to individually guide the usage of AEDs.Foramen ovale (FO) electrodes are a semi-invasive alternative to stereo-EEG electrodes. After general endotracheal anesthesia is induced, a single multi-contact FO electrode is positioned adjacent to each mTL inserted through the cheek skin to the ipsilateral natural aperture (FO) in the skull . High-quFor patients with AD, routine scalp EEG monitoring (20–40 min) is both necessary and feasible for the purpose of observing brain rhythms, but it is less helpful in recording epileptiform activity . HoweverMany studies have demonstrated that though there is a lack of visible epileptiform activity on scalp EEG, non-specific subtle and quantitative changes in local or long-distance networks induced by mTL spikes or seizures  can be dDevelopment of computational approaches that accurately identify spikes or seizures from scalp EEG or MEG is underway . HoweverPatients with AD have an increased risk of seizures, compared to an age-matched control population, and they are over 10 times more likely to develop epilepsy, especially patients with early onset familial AD . The sigHowever, one thing most studies agree on is that patients with AD with seizures or subclinical epileptiform activity experience faster cognitive declines over time , and patTo date, human studies have identified several candidate EEG biomarkers as follows: epileptiform discharges , frequent SSSs, TIRDA, and PSWEs recorded by scalp EEG . In addiTY chose the search term, determined the methodology of the review, and drafted the manuscript. XL helped to choose the search term and determined the methodology of the review. JW and QW revised the manuscript and commented on previous versions of the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Dear readers,The Journal of Innovations in Cardiac Rhythm Management contains an important article by Mehta et al. titled “Utility of Ischemia Testing Prior to Ablation for Sustained Monomorphic Ventricular Tachycardia.” In this retrospective study, the authors compared clinical outcomes between patients who underwent ischemia workup and revascularization prior to ablation for monomorphic ventricular tachycardia (VT) and those who did not.1 They found that an ischemia workup, although frequently completed, infrequently led to revascularization and did not have a clinical benefit.This issue of Although this study is retrospective and included only a small number of patients, it is important because it highlights a common and clinically relevant topic. Although monomorphic VT has been well established as a result of pre-existing scar, ischemia evaluation continues to be performed in a large number of cases prior to VT ablation. There are a few possible reasons for this practice, and some are discussed in the abovementioned article. Some prior reports and studies reported a reduction or elimination of VT after revascularization. Another reason could be a concern for hemodynamic instability during ablation in the presence of flow-limiting coronary artery disease. However, it is rare that such practice results in revascularization and improvement of the clinical outcome. A pre-ablation ischemia workup also carries some disadvantages, perhaps the most significant of which is a delay in the procedure.This study is hypothesis-generating and hopefully will lead to a prospective clinical trial. Until then, I believe that it might impact clinical practice and would lead some clinicians, including myself, to pause a bit before performing an ischemia workup prior to ablation and instead perhaps adopt an individualized approach where such testing is performing only in situations of a clear threat of hemodynamic instability during the procedure secondary to ischemia.The Journal of Innovations in Cardiac Rhythm Management.I hope that you enjoy reading this issue of Sincerely,md, fhrs, faccMoussa Mansour, Editor in ChiefThe Journal of Innovations in Cardiac Rhythm ManagementMMansour@InnovationsInCRM.comDirector, Atrial Fibrillation ProgramJeremy Ruskin and Dan Starks Endowed Chair in CardiologyMassachusetts General HospitalBoston, MA 02114