Evaluating Musculoskeletal Anatomy Knowledge Among First-Year Medical Students: Comparison of Anatomy Examination Scores Between Cohorts Receiving a Pre-Examination Review Session Versus Focused Clinical Skills Teaching Session
Bibliographic record
Abstract
Introduction: Musculoskeletal (MSK) complaints are among the most common reasons patients seek medical care in the US. Appropriate management of MSK problems requires foundational knowledge of MSK anatomy. However, several studies in the US and Canada have highlighted insufficient anatomy knowledge among medical students and residents. Objective: This study evaluated MSK anatomy final examination scores between two successive cohorts of first-year medical students to compare two interventions: (1) a teaching-assistant-led pre-examination review session in the first cohort; (2) a clinical skills teaching session of the knee and shoulder joints, which was reinforced during anatomy dissection sessions within the same semester of the second cohort. Methods: The Class of 2025 received traditional anatomy education during their first year of medical school, as well as a pre-examination review session led by teaching assistants. The Class of 2026 received the traditional anatomy curriculum without the benefits of a pre-examination review session led by teaching assistants. Instead, the Class of 2026 received a newly added clinical skills teaching involving the knee and shoulder joints. We analyzed scores from a 30-question multiple-choice anatomy final examination completed during March of each class’s first year of medical school. Of these 30 questions, six were directly related to the muscles, ligaments, and bones of the knee and shoulder. Unpaired two-tailed t-tests were used to determine statistical significance of both comparisons. Results: Scores from all 77 students in each class were analyzed. For the entire test of 30 questions, the Class of 2025 (who received the pre-examination review session) did not perform significantly better than the Class of 2026 (average scores 84.7% versus 82.3%, p=0.142). For the 6 knee/shoulder-specific questions, the Class of 2026 (who received focused clinical skills teaching of the knee and shoulder joints) also did not perform significantly better than the Class of 2025 (average scores 92.0% versus 91.6 %, p=0.845). Interestingly, within the same cohort, students in the Class of 2026 scored 12.1% higher on the 6 knee and shoulder questions than on the remaining 24 questions. On the other hand, students in the Class of 2025 scored only 8.6% higher on those 6 questions than on the remaining 24 questions. The difference (12.1% versus 8.6%) approached significance (p=0.06) but did not achieve statistical significance at p<0.05. Discussion: Despite not having a pre-exam review session, the Class of 2026 did not score significantly worse than the Class of 2025 on either the 30 general MSK questions or the 6 knee/shoulder-specific questions. It is interesting that the difference between the 6-question and 24-question averages among the two classes approached significance (p=0.06) but did not achieve statistical significance at p<0.05. Longitudinal assessments may further elucidate the differential effects of pre-examination review sessions and clinical skills teaching on medical students’ anatomy knowledge of the musculoskeletal system. Target Audience: US and Canada medical school education directors; preclinical medical students.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".