Assessing the impact of musical proficiency on percussion note identification skills in undergraduate medical students: an analysis using the Montreal Battery of Evaluation of Amusia
Bibliographic record
Abstract
BACKGROUND: The art of percussion in physical examination is a critical skill for clinicians, offering insights into the condition of internal organs. Historical ties between music and medicine, exemplified by Apollo, the Greek God of both domains, suggest a potential correlation between musical aptitude and clinical acumen. This study investigates the relationship between musical abilities, as measured by the Montreal Battery of Evaluation of Amusia (MBEA), and the proficiency in identifying percussion notes among medical students. METHODS: A cross-sectional study was conducted with 250 pre-clinical undergraduate medical students from the state of Uttar Pradesh, India. Participants completed the MBEA, which assesses various aspects of music processing, along with a self-engineered percussion sound test. The percussion test involved identifying different percussion notes from clinical demonstration videos. Correlation and regression analyses were performed to evaluate relationships between MBEA scores, prior musical training, and proficiency in identifying percussion notes. Statistical significance was determined at p < 0.05. RESULTS: Among 250 participants, 38.8% had prior musical experience. MBEA scores weakly correlated with percussion competency (r≈0.18, R^2 = 0.033, p = 0.003), while prior training in music strongly correlated with MBEA scores (r≈0.89, p < 0.001) and modestly with percussion competency (r≈0.23, p < 0.001). Logistic regression revealed variability in predictive accuracy, highlighting the complex interplay of factors influencing auditory skills and supporting music education as a supplementary tool in medical training. CONCLUSION: The study indicates a minimal correlation between musical aptitude and the ability to discern percussion notes, highlighting the complexity of auditory perception skills among medical students. While prior musical experience shows a stronger association with MBEA scores, the overall impact on clinical percussion skills appears limited. These findings suggest that while there may be a relationship between musical training and auditory skills, it is not a strong predictor of clinical percussion proficiency.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".