Educating residents in spine surgery: A study of Entrustable professional activities in neurosurgery and orthopedic surgery.

BACKGROUND: Surgery for spinal disorders represents some of the commonest surgical procedures performed in many countries worldwide, carried out by neurosurgeons and orthopedic surgeons. Residency training is shifting to competency-based medical education, which requires setting standards for graduating residents and their assessments. However, gaps exist in the literature regarding the parameters used for assessment and the mastery levels expected of graduating residents in the performance of common spinal procedures as defined in Entrustable Professional Activities (EPAs). The objectives of the study were to describe the assessment parameters used for residents, identify the standard of performance expected of graduating residents of EPAs of spinal procedures, and identify factors predicting the expected standard of competent performance of graduating residents. METHODS: The survey was sent to neurosurgery and orthopedic surgery Faculty requesting their recommendations on parameters of assessment and the expected standard competence performance for EPAs related to spinal procedures using our entrustment scale (A-E). RESULTS: Based on total responses, the recommended number of assessments and assessors for each EPA was 5 and 2, respectively. Regarding each specialty, there was no significant difference in the recommended number of assessments for each EPA. However, neurosurgery Faculty recommended higher number of assessors(n = 3) than orthopedic surgery Faculty(n = 2) for both posterior spinal decompression EPA(PSD) (p = 0.01) and spinal instrumentation EPA(SI) (p = 0.04). Based on total responses, 83% felt PSD was appropriate to the general practice, 86.8% considered it not too broad, and 62.3% expected entrustment level E as a graduation target. The proportions of these ratings were slightly lower for SI at 58.5%, 71.7% and 56.6%, respectively. Both specialties indicated that the EPAs were not too broad. In contrast, neurosurgery Faculty were more likely to consider these EPAs appropriate for general practice than orthopedic surgery Faculty for both PSD (94.7% vs 53.3%, p = 0.0003) and SI (68.4% vs 33.3%, p = 0.02). Moreover, neurosurgery Faculty had a higher expected standard of performance as a graduation target for both PSD (Level E 76.3% vs 26.7%, p = 0.001) and SI (Level E 65.8% vs 33.3%, p = 0.03) than orthopedic surgery Faculty. Expectations of entrustment level E for PSD was associated with the belief that the current EPA was appropriate for the general practice of their specialty with an odds ratio of 8.35 (p = 0.01, 95%CI 1.53-45.67). CONCLUSIONS: A difference exists in parameters of assessment and expected standard competence performance of spine procedures among spinal surgery specialties. In our opinion, there should be efforts to develop consensus between specialties for the sake of uniform delivery of high-quality care for patients regardless of the specialty of their surgeon. Our results will be particularly valuable to certification bodies in the assessment of spinal milestones. This study has important implications for the design of residency and fellowship education in spinal surgery internationally.

Quality of Life Factors and Measurement in Adult Meningioma Patients: A Systematic Review.

BACKGROUND: Meningiomas are common brain neoplasms that can significantly influence health-related quality of life (HRQOL), yet the factors influencing HRQOL in adult patients remain unclear. We aimed to bridge this knowledge gap by determining these key factors. METHODS: We conducted a systematic review, searching EMBASE, MEDLINE, CINAHL, Scopus and PsycINFO up to February 2024. We included original, peer-reviewed studies focusing on adult patients (>18 years) with current or past meningioma at any stage of treatment that measured HRQOL or its proxies in relation to patient-, tumour- and treatment-related factors. Two independent reviewers screened abstracts and full-texts, selecting studies with an acceptable risk of bias for data extraction and narrative synthesis. The protocol of this review was registered on PROSPERO (# CRD42023431097). RESULTS: Of N = 3002 studies identified, N = 31 studies were included. Key factors found to influence HRQOL in adult meningioma patients include surgery, radiotherapy, neurological function, functional status, comorbidities, sleep quality, psychological impairment, age and employment. Factors related to tumour characteristics yielded inconsistent findings. Heterogeneity and inconsistencies in HRQOL measurement across studies hindered definitive conclusions about the impact of factors on HRQOL. CONCLUSION: Our review elucidates the multifaceted influences on HRQOL in meningioma patients, with significant variability due to patient-, tumour- and treatment-related factors. We emphasize the need for standardized, disease-specific HRQOL assessments in meningioma patients. Collaborative efforts towards consistent, large-scale, prospective research are essential to comprehensively understand and improve HRQOL, thereby enhancing tailored care for this population.

Causal Forest Machine Learning Analysis of Parkinson's Disease in Resting-State Functional Magnetic Resonance Imaging.

In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson's disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F1 score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson's Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups.

Impact of Automated Prognostication on Traumatic Brain Injury Care: A Focus Group Study.

BACKGROUND: Prognosticating outcomes for traumatic brain injury (TBI) patients is challenging due to the required specialized skills and variability among clinicians. Recent attempts to standardize TBI prognosis have leveraged machine learning (ML) methodologies. This study evaluates the necessity and influence of ML-assisted TBI prognostication through healthcare professionals' perspectives via focus group discussions. METHODS: Two virtual focus groups included ten key TBI care stakeholders (one neurosurgeon, two emergency clinicians, one internist, two radiologists, one registered nurse, two researchers in ML and healthcare and one patient representative). They answered six open-ended questions about their perceptions and potential ML use in TBI prognostication. Transcribed focus group discussions were thematically analyzed using qualitative data analysis software. RESULTS: The study captured diverse perceptions and interests in TBI prognostication across clinical specialties. Notably, certain clinicians who currently do not prognosticate expressed an interest in doing so independently provided they had access to ML support. Concerns included ML's accuracy and the need for proficient ML researchers in clinical settings. The consensus suggested using ML as a secondary consultation tool and promoting collaboration with internal or external research resources. Participants believed ML prognostication could enhance disposition planning and standardize care regardless of clinician expertise or injury severity. There was no evidence of perceived bias or interference during the discussions. CONCLUSION: Our findings revealed an overall positive attitude toward ML-based prognostication. Despite raising multiple concerns, the focus group discussions were particularly valuable in underscoring the potential of ML in democratizing and standardizing TBI prognosis practices.

Development of a Multimodal Machine Learning-Based Prognostication Model for Traumatic Brain Injury Using Clinical Data and Computed Tomography Scans: A CENTER-TBI and CINTER-TBI Study.

Computed tomography (CT) is an important imaging modality for guiding prognostication in patients with traumatic brain injury (TBI). However, because of the specialized expertise necessary, timely and dependable TBI prognostication based on CT imaging remains challenging. This study aimed to enhance the efficiency and reliability of TBI prognostication by employing machine learning (ML) techniques on CT images. A retrospective analysis was conducted on the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) data set (n = 1016). An ML-driven binary classifier was developed to predict favorable or unfavorable outcomes at 6 months post-injury. The prognostic performance was assessed using the area under the curve (AUC) over fivefold cross-validation and compared with conventional models that depend on clinical variables and CT scoring systems. An external validation was performed using the Comparative Indian Neurotrauma Effectiveness Research in Traumatic Brain Injury (CINTER-TBI) data set (n = 348). The developed model achieved superior performance without the necessity for manual CT assessments (AUC = 0.846 [95% CI: 0.843-0.849]) compared with the model based on the clinical and laboratory variables (AUC = 0.817 [95% CI: 0.814-0.820]) and established CT scoring systems requiring manual interpretations (AUC = 0.829 [95% CI: 0.826-0.832] for Marshall and 0.838 [95% CI: 0.835-0.841] for International Mission for Prognosis and Analysis of Clinical Trials in TBI [IMPACT]). The external validation demonstrated the prognostic capacity of the developed model to be significantly better (AUC = 0.859 [95% CI: 0.857-0.862]) than the model using clinical variables (AUC = 0.809 [95% CI: 0.798-0.820]). This study established an ML-based model that provides efficient and reliable TBI prognosis based on CT scans, with potential implications for earlier intervention and improved patient outcomes.

Effect of Cobalt-60 Treatment Dose Rate on Arteriovenous Malformation Obliteration After Stereotactic Radiosurgery With Gamma Knife.

BACKGROUND AND OBJECTIVES: Stereotactic radiosurgery (SRS) marginal dose is associated with successful obliteration of cerebral arteriovenous malformations (AVM). SRS dose rate-how old the cobalt-60 sources are-is known to influence outcomes for some neurological conditions and benign tumors. The objective of this study was to determine the association between cobalt-60 treatment dose rate and cerebral AVM obliteration in patients treated with SRS. METHODS: We performed a retrospective cohort study of 361 patients undergoing 411 AVM-directed SRS treatments between 2005 and 2019 at a single institution. Lesion characteristics, SRS details, obliteration dates, and post-treatment toxicities were recorded. Univariate and multivariate regression analyses of AVM outcomes regarding SRS dose rate (range 1.3-3.7 Gy, mean = 2.4 Gy, median = 2.5 Gy) were performed. RESULTS: At 10 years post-SRS, 68% of AVMs were obliterated on follow-up imaging. Dose rates >2.9 Gy/min were found to be significantly associated with AVM obliteration compared with those <2.1 Gy/min ( P = .034). AVM size, biologically effective dose, and SRS marginal dose were also associated with obliteration, with obliteration more likely for smaller lesions, higher biologically effective dose, and higher marginal dose. Higher dose rates were not associated with the development of post-SRS radiological or symptomatic edema, although larger AVM volume was associated with both types of edema. CONCLUSION: Patients with cerebral AVMs treated with higher SRS dose rates (from newer cobalt-60 sources) experience higher incidences of obliteration without a significant change in the risk of post-treatment edema.

Levels of nasal nitric oxide and nitric oxide synthase expression in chronic rhinosinusitis with nasal polyposis.

KEY POINTS: CRSwNP patients had decreased nNO and increased SNOT-22, endoscopy, and CT scores. CRSwNP patients exhibited decreased nNO despite elevated iNOS and eNOS mRNA expression. The mechanism behind lowered nNO in CRSwNP may not be related to NOS expression.

An interrupted time series evaluation of the effect of cannabis legalization on intentional self-harm in two Canadian provinces: Ontario and Alberta. / Évaluation, à l'aide de séries chronologiques interrompues, de l'effet de la légalisation du cannabis sur les lésions auto-infligées dans deux provinces canadiennes : Ontario et Alberta.

INTRODUCTION: Despite the association between cannabis use and higher prevalence of suicidal ideation and attempt, the effect of cannabis legalization and regulation in Canada on intentional self-harm has not been determined. METHODS: We used an interrupted time series of population-based rates of emergency department (ED) visits and hospitalizations for intentional self-harm per 100 000 in Ontario and Alberta from January/April 2010 to February 2020. Aggregate monthly counts of ED visits and hospitalizations for intentional self-harm (ICD-10 codes X60-X84, R45.8) were obtained from the National Ambulatory Care Reporting System and Discharge Abstract Database, respectively. RESULTS: The legalization and regulation of cannabis in Canada was not significantly associated with a change in rates of ED visits for intentional self-harm in Ontario (level = 0.58, 95% CI: -1.14 to 2.31; trend = -0.17, 95% CI: -0.35 to 0.01) or Alberta (level = -0.06, 95% CI: -2.25 to 2.12; trend = -0.07, 95% CI: -0.27 to 0.13). Hospitalizations for intentional self-harm also remained unchanged in Ontario (level = -0.14, 95% CI: -0.48 to 0.20; trend = 0.01, 95% CI: -0.03 to 0.04) and Alberta (level = -0.41, 95% CI: -1.03 to 0.21; trend = -0.03, 95% CI: -0.08 to 0.03). CONCLUSION: Legalization and regulation of cannabis in Canada has not increased rates of ED visits or hospitalizations for intentional self-harm in Ontario and Alberta. Individual-level analyses that account for demographic characteristics and include other provinces and territories are needed.

Fractionated radiotherapy for surgically resected intracranial meningiomas: A multicentre retrospective cohort study.

BACKGROUND: Aside from surgical resection, the only standard of care treatment modality for meningiomas is radiotherapy (RT). Despite this, few studies have focused on identifying clinical covariates associated with failure of fractionated RT following surgical resection (fRT), and the timing of fRT following surgery still remains controversial (adjuvant versus salvage fRT). We assessed the outcomes of the largest, multi-institutional cohort of surgically resected meningiomas treated with subsequent adjuvant and salvage fRT to identify factors associated with local freedom from recurrence (LFFR) over 3-10 years post-fRT and to determine the optimal timing of fRT. METHODS: Patients with intracranial meningiomas who underwent surgery and fRT between 1997 and 2018 were included. Primary endpoints were radiographic recurrence/progression and time to progression from the completion of fRT. RESULTS: 404 meningiomas were included for analysis. Of these, 167 (41.3%) recurred post-fRT. Clinical covariates independently associated with worse PFS post-fRT included receipt of previous RT to the meningioma, having a WHO grade 3 meningioma or recurrent meningioma, the meningioma having a higher MIB1-index or brain invasion on pathology, and older patient age at diagnosis. Subgroup analysis identified higher MIB1-index as a histological factor associated with poorer LFFR in WHO grade 2 meningiomas. 179 patients underwent adjuvant RT shortly after surgery whereas 225 patients had delayed, salvage fRT after recurrence/progression. Following propensity score matching, patients that underwent adjuvant fRT had improved LFFR post-fRT compared to those that received salvage fRT. CONCLUSION: There is a paucity of clinical factors that can predict a meningioma's response to fRT following surgery. Adjuvant fRT may be associated with improved PFS post-fRT compared to salvage fRT. Molecular biomarkers of RT-responsiveness are needed to better inform fRT treatment decisions.

Astrogliosis marker 11C-SL25.1188 PET in traumatic brain injury with persistent symptoms.

Traumatic brain injury (TBI) is common but little is known why up to a third of patients have persisting symptoms. Astrogliosis, a pathophysiological response to brain injury, may be a potential therapeutic target, but demonstration of astrogliosis in the brain of humans with TBI and persistent symptoms is lacking. Astroglial marker monoamine oxidase B (MAO-B) total distribution volume (11C-SL25.1188 VT), an index of MAO-B density, was measured in 29 TBI and 29 similarly aged healthy control cases with 11C-SL25.1188 PET, prioritizing prefrontal cortex (PFC) and cortex proximal to cortical convexity. Correlations of PFC 11C-SL25.1188 VT with psychomotor and processing speed; and serum blood measures implicated in astrogliosis were determined. 11C-SL25.1188 VT was greater in TBI in PFC (P = 0.00064) and cortex (P = 0.00038). PFC 11C-SL25.1188 VT inversely correlated with Comprehensive Trail Making Test psychomotor and processing speed (r = -0.48, P = 0.01). In participants scanned within 2 years of last TBI, PFC 11C-SL25.1188 VT correlated with serum glial fibrillary acid protein (r = 0.51, P = 0.037) and total tau (r = 0.74, P = 0.001). Elevated 11C-SL25.1188 VT argues strongly for astrogliosis and therapeutics modifying astrogliosis towards curative phenotypes should be tested in TBI with persistent symptoms. Given substantive effect size, astrogliosis PET markers should be applied to stratify cases and/or assess target engagement for putative therapeutics targeting astrogliosis.

Automated screening of computed tomography using weakly supervised anomaly detection.

BACKGROUND: Current artificial intelligence studies for supporting CT screening tasks depend on either supervised learning or detecting anomalies. However, the former involves a heavy annotation workload owing to requiring many slice-wise annotations (ground truth labels); the latter is promising, but while it reduces the annotation workload, it often suffers from lower performance. This study presents a novel weakly supervised anomaly detection (WSAD) algorithm trained based on scan-wise normal and anomalous annotations to provide better performance than conventional methods while reducing annotation workload. METHODS: Based on surveillance video anomaly detection methodology, feature vectors representing each CT slice were trained on an AR-Net-based convolutional network using a dynamic multiple-instance learning loss and a center loss function. The following two publicly available CT datasets were retrospectively analyzed: the RSNA brain hemorrhage dataset (normal scans: 12,862; scans with intracranial hematoma: 8882) and COVID-CT set (normal scans: 282; scans with COVID-19: 95). RESULTS: Anomaly scores of each slice were successfully predicted despite inaccessibility to any slice-wise annotations. Slice-level area under the curve (AUC), sensitivity, specificity, and accuracy from the brain CT dataset were 0.89, 0.85, 0.78, and 0.79, respectively. The proposed method reduced the number of annotations in the brain dataset by 97.1% compared to an ordinary slice-level supervised learning method. CONCLUSION: This study demonstrated a significant annotation reduction in identifying anomalous CT slices compared to a supervised learning approach. The effectiveness of the proposed WSAD algorithm was verified through higher AUC than existing anomaly detection techniques.

The effect of recreational cannabis legalization on rates of traffic injury in Canada.

AIMS: To measure the impact of Canada's recreational cannabis legalization (RCL) in October 2018 and the subsequent impact of the coronavirus disease 2019 (COVID-19) lockdowns from March 2020 on rates of emergency department (ED) visits and hospitalizations for traffic injury. DESIGN: An interrupted time series analysis of rates of ED visits and hospitalizations in Canada recorded in population-based databases from January/April 2010 to March 2021. SETTING: ED visits in Ontario and Alberta and hospitalizations in Ontario, Alberta, British Columbia, the Prairies (Manitoba and Saskatchewan) and the Maritimes (Nova Scotia, New Brunswick, Newfoundland and Prince Edward Island). PARTICIPANTS: Monthly counts of presentations to the ED or hospital for motor vehicle injury or pedestrian/cyclist injury, used to calculate monthly rates per 100 000 population. MEASUREMENTS: An occurrence of one or more International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Canada (ICD-10-CA) code for motor vehicle injury (V20-V29, V40-V79, V30-V39 and V86) and pedestrian/cyclist injury (V01-V09 and V10-V19) within the National Ambulatory Care Reporting System and Discharge Abstract Database. FINDINGS: There were no statistically significant changes in rates of ED visits and hospitalizations for motor vehicle or pedestrian/cyclist injury after RCL after accounting for multiple testing. After COVID-19, there was an immediate decrease in the rate of ED visits for motor vehicle injury that was statistically significant only in Ontario (level change ß = -16.07 in Ontario, 95% CI = -20.55 to -11.60, P = 0.000; ß = -10.34 in Alberta, 95% CI = -17.80 to -2.89, P = 0.008; α of 0.004) and no changes in rates of hospitalizations. CONCLUSIONS: Canada's recreational cannabis legalization did not notably impact motor vehicle and pedestrian/cyclist injury. The rate of emergency department visits for motor vehicle injury decreased immediately after COVID-19 lockdowns, resulting in rates below post-recreational cannabis legalization levels in the year after COVID-19.

Predicting Severity of Head Collision Events in Elite Soccer Using Preinjury Data: A Machine Learning Approach.

OBJECTIVE: To develop machine learning (ML) models that predict severity of head collision events (HCEs) based on preinjury variables and to investigate which variables are important to predicting severity. DESIGN: Data on HCEs were collected with respect to severity and 23 preinjury variables to create 2 datasets, a male dataset using men's tournaments and mixed dataset using men's and women's tournaments, to perform ML analysis. Machine learning analysis used a random forest classifier based on preinjury variables to predict HCE severity. SETTING: Four elite international soccer tournaments. PARTICIPANTS: Elite athletes participating in analyzed tournaments. INDEPENDENT VARIABLES: The 23 preinjury variables collected for each HCE. MAIN OUTCOME MEASURES: Predictive ability of the ML models and association of important variables. RESULTS: The ML models had an average area under the receiver operating characteristic curve for predicting HCE severity of 0.73 and 0.70 for the male and mixed datasets, respectively. The most important variables for prediction were the mechanism of injury and the event before injury. In the male dataset, the mechanisms "head-to-head" and "knee-to-head" were together significantly associated ( P = 0.0244) with severity; they were not significant in the mixed dataset ( P = 0.1113). In both datasets, the events "corner kicks" and "throw-ins" were together significantly associated with severity (male, P = 0.0001; mixed, P = 0.0004). CONCLUSIONS: ML models accurately predicted the severity of HCE. The mechanism and event preceding injury were most important for predicting severity of HCEs. These findings support the use of ML to inform preventative measures that will mitigate the impact of these preinjury factors on player health.

Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

BACKGROUND: The purpose of this study was to conduct a systematic review for understanding the availability and limitations of artificial intelligence (AI) approaches that could automatically identify and quantify computed tomography (CT) findings in traumatic brain injury (TBI). METHODS: Systematic review, in accordance with PRISMA 2020 and SPIRIT-AI extension guidelines, with a search of 4 databases (Medline, Embase, IEEE Xplore, and Web of Science) was performed to find AI studies that automated the clinical tasks for identifying and quantifying CT findings of TBI-related abnormalities. RESULTS: A total of 531 unique publications were reviewed, which resulted in 66 articles that met our inclusion criteria. The following components for identification and quantification regarding TBI were covered and automated by existing AI studies: identification of TBI-related abnormalities; classification of intracranial hemorrhage types; slice-, pixel-, and voxel-level localization of hemorrhage; measurement of midline shift; and measurement of hematoma volume. Automated identification of obliterated basal cisterns was not investigated in the existing AI studies. Most of the AI algorithms were based on deep neural networks that were trained on 2- or 3-dimensional CT imaging datasets. CONCLUSION: We identified several important TBI-related CT findings that can be automatically identified and quantified with AI. A combination of these techniques may provide useful tools to enhance reproducibility of TBI identification and quantification by supporting radiologists and clinicians in their TBI assessments and reducing subjective human factors.

Protective responses of older adults for avoiding injury during falls: evidence from video capture of real-life falls in long-term care.

BACKGROUND: falls are common in older adults, and any fall from standing height onto a rigid surface has the potential to cause a serious brain injury or bone fracture. Safe strategies for falling in humans have traditionally been difficult to study. OBJECTIVE: to determine whether specific 'safe landing' strategies (body rotation during descent, and upper limb bracing) separate injurious and non-injurious falls in seniors. DESIGN: observational cohort study. SETTING: two long-term care homes in Vancouver BC. METHODS: videos of 2,388 falls experienced by 658 participants (mean age 84.0 years; SD 8.1) were analysed with a structured questionnaire. General estimating equations were used to examine how safe landing strategies associated with documented injuries. RESULTS: injuries occurred in 38% of falls, and 4% of falls caused injuries treated in hospitals. 32% of injuries were to the head. Rotation during descent was common and protective against injury. In 43% of falls initially directed forward, participants rotated to land sideways, which reduced their odds for head injury 2-fold. Upper limb bracing was used in 58% of falls, but rather than protective, bracing was associated with an increased odds for injury, possibly because it occurred more often in the demanding scenario of forward landings. CONCLUSIONS: the risk for injury during falls in long-term care was reduced by rotation during descent, but not by upper limb bracing. Our results expand our understanding of human postural responses to falls, and point towards novel strategies to prevent fall-related injuries.

Social determinants of health associated with psychological distress stratified by lifetime traumatic brain injury status and sex: Cross-sectional evidence from a population sample of adults in Ontario, Canada.

This study identified the social determinants of health (SDoH) associated with psychological distress in adults with and without a self-reported history of traumatic brain injury (TBI), stratified by sex. Data from the 2014-2017 cycles of the Centre for Addiction and Mental Health Monitor Survey, a representative survey of adults ≥18 years in Ontario, Canada, were analyzed (N = 7,214). The six-item version of the Kessler Psychological Distress Scale was used to determine moderate to severe psychological distress. Self-reported lifetime TBI was defined as a head injury resulting in a loss of consciousness for ≥5 minutes or at least one-night stay in the hospital (16.4%). Among individuals reporting a history of TBI, 30.2% of males and 40.1% of females reported psychological distress (p = 0.0109). Among individuals who did not report a history of TBI, 17.9% of males and 23.5% of females reported psychological distress (p<0.0001). Multivariable logistic regression analyses showed that the SDoH significantly associated with elevated psychological distress were similar between individuals with and without a history of TBI. This included unemployment, student, or 'other' employment status among both males and females; income below the provincial median and age 65+ among males; and rural residence among females. This study highlighted opportunities for targeted population-level interventions, namely accessible and affordable mental health supports for individuals with lower income. Notably, this study presented evidence suggesting adaptations to existing services to accommodate challenges associated with TBI should be explored, given the finite and competing demands for mental health care and resources.

Validity and Usefulness of YouTube Videos Related to Endoscopic Transsphenoidal Surgery for Patient Information.

Objectives This article evaluates the completeness and accuracy of YouTube videos related to endoscopic transsphenoidal surgery (ETS) as a source for patient information. Design YouTube was searched using relevant terms pertaining to ETS. Videos were evaluated independently by two physician reviewers experienced in ETS. Video demographics including uploader source along with validity scores based on predetermined checklists were captured. Setting Internet. Participants Not applicable. Main Outcome Measures A novel ETS scoring checklist, the modified DISCERN criteria, and Journal of the American Medical Association (JAMA) benchmark score were used to measure completeness and accuracy of videos. video power index (VPI) was calculated to reflect popularity. Intraclass correlation coefficient was calculated for rater agreement. Results Seventy-nine videos were included in final scoring and analysis. The ETS score, DISCERN, JAMA, and mean VPI across all included videos were 5.0 ± 2.7, 2.4 ± 0.83, 2.19 ± 0.62, and 8.92 ± 18.1, respectively. Based on the ETS score checklist, 31 (39%) of the videos were rated as poor, 30 (38%) were moderately useful, 17 (22%) were useful, and 1 (1%) was exceptional. There was a significant positive correlation between the ETS, DISCERN, and JAMA scores ( p < 0.001), but no correlation with VPI and the validity scores. There were no significant differences comparing validity scores based on the uploader source. Conclusion YouTube videos related to ETS have limited usefulness and poor overall validity for patient information. Clinicians should direct patients to other validated sources of information and aim to improve the comprehensiveness of ETS-related videos.