Outcomes of the Canadian Orthopaedic Surgery Medical Education Course (COSMEC): a virtual curriculum to enhance medical student learning
Bibliographic record
Abstract
Background: Studies have highlighted inadequate exposure to musculoskeletal education and orthopedic surgery in mandatory medical school curricula; thus, the Canadian Orthopaedic Surgery Medical Education Course (COSMEC) was designed to enhance medical education around orthopedic surgery and common musculoskeletal presentations encountered in primary care. We sought to explore the effectiveness of COSMEC in preparing medical students for clinical training and future practice. Methods: Canadian and international medical students were invited to participate in COSMEC, a 12-week virtual course led by orthopedic faculty and senior residents. Teaching objectives were guided by the musculoskeletal objectives of the Medical Council of Canada Qualifying Examination and expert opinion. We administered pre- and postcourse surveys to assess outcomes related to participant knowledge, confidence, and interest in orthopedic surgery. Results: A total of 133 medical students registered and completed COSMEC. Of these, we received 84 paired pre- and postcourse surveys. Knowledge scores improved from 7.9 (standard deviation [SD] 2.6) to 9.7 (SD 2.0) out of 14 (p < 0.001). There were significant improvements in participant-reported confidence in performing a history and physical examination, understanding the basic components of fracture management, managing bone and joint emergencies, and describing fracture radiographs (p < 0.001). Conclusion: Overall, COSMEC enhanced knowledge and confidence in orthopedic and musculoskeletal topics and is an effective extracurricular learning resource for medical students. It can help prepare medical students for future training and practice involving orthopedic and musculoskeletal patient presentations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".