Determination of clinically relevant content for a musculoskeletal anatomy curriculum for physical medicine and rehabilitation residents
Bibliographic record
Abstract
To address the need for more clinical anatomy training in residency education, many postgraduate programs have implemented structured anatomy courses into their curriculum. Consensus often does not exist on specific content and level of detail of the content that should be included in such curricula. This article describes the use of the Delphi method to identify clinically relevant content to incorporate in a musculoskeletal anatomy curriculum for Physical Medicine and Rehabilitation (PM&R) residents. A two round modified Delphi involving PM&R experts was used to establish the curricular content. The anatomical structures and clinical conditions presented to the expert group were compiled using multiple sources: clinical musculoskeletal anatomy cases from the PM&R residency program at the University of Toronto; consultation with PM&R experts; and textbooks. In each round, experts rated the importance of each curricular item to PM&R residency education using a five-point Likert scale. Internal consistency (Cronbach's alpha) was used to determine consensus at the end of each round and agreement scores were used as an outcome measure to determine the content to include in the curriculum. The overall internal consistency in both rounds was 0.99. A total of 37 physiatrists from across Canada participated and the overall response rate over two rounds was 97%. The initial curricular list consisted of 361 items. After the second iteration, the list was reduced by 44%. By using a national consensus method we were able to objectively determine the relevant anatomical structures and clinical musculoskeletal conditions important in daily PM&R practice.
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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 | 0.023 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".