Musculoskeletal Learning and Knowledge Retention Among Postgraduate Physicians: Evaluating the Long-Term Impact of a New Preclerkship Curriculum at a Nationally Accredited Medical Program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: Musculoskeletal (MSK) injuries and disorders are exceptionally prevalent in the clinical setting. Despite this, physician training in MSK medicine has been historically inadequate contributing to a lack of MSK knowledge, confidence, and clinical skills among postgraduate physicians. The goal of this investigation was to examine the long-term impact of a new preclerkship MSK curriculum implemented by a nationally accredited medical program on postgraduate physician's learning and knowledge retention in the area of MSK medicine. Methods: Five hundred sixty-eight postgraduate physicians (years 1-6) who had previously completed the new curriculum over a 6-year period were recruited to complete a standardized and validated MSK examination that consisted of 30 multiple-choice questions on core or must-know topics in MSK medicine that could be directly mapped to learning objectives within the new preclerkship MSK curriculum. Results: Ninety postgraduate physicians completed the examination, obtaining an average score of 75.0% (±10.2; range 57.0-100.0). Physicians who completed MSK-related electives during clerkship training or specialized in fields related to MSK medicine (i.e., orthopaedics, PM&R, sports medicine, and rheumatology) performed significantly better on the MSK examination (p ≤ 0.01). Conclusion: Data indicated that the program's new preclerkship curriculum supports high levels of MSK learning and knowledge retention among postgraduate physicians. These findings are expected to assist with the establishment of minimum curriculum standards and can be used to guide MSK curricular reform at other medical programs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it