A scoping review on bolstering concussion knowledge in medical education
Bibliographic record
Abstract
Abstract Background Concussions are a public health concern. Underdiagnosis and mismanagement negatively impact patients, risking in persistent symptoms and permanent disability. Objective This scoping review consolidates the heterogeneous and inconsistent concussion research and identifies key areas for medical education curriculum design to focus on for effective knowledge acquisition and bolstering competency in family physician residency. We analyze the literature on concussion education spanning various healthcare disciplines in North America. Methods PRISMA-Sc was followed and MEDLINE and EMBASE Classic + EMBASE in the OvidSP search platform were used to find terms for brain concussion AND medical education OR specific education until 2021. Results There are significant knowledge gaps about concussions, increased clinical exposure is required for competency which bolster physical examination skills and streamlined concussion guidelines are required for family medicine specialists that filter undifferentiated symptoms25% of participants improved adherence to concussion guidelines after an educational intervention and knowledge increased after concussion workshop and clinics. Conclusions Multifaceted teaching improves concussion diagnosis and management. More research is needed to examine concussion competency and, more importantly, whether these interventions improve patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".