Musculoskeletal Learning and Knowledge Retention Among Postgraduate Physicians: Evaluating the Long-Term Impact of a New Preclerkship Curriculum at a Nationally Accredited Medical Program
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".