From Music to Medicine: Are Pianists at an Advantage When Learning Surgical Skills?
Bibliographic record
Abstract
Background: The acquisition of procedural competence is of vital importance in the training of physicians. It has been observed that medical students with extensive musical backgrounds often learn surgical techniques more rapidly than other students, raising the question of motor skill transfer from one area to another. Objective: It is the aim of this project to explore whether musicians can learn and perform surgical skills more rapidly than non-musicians. This study explores the claims that musicians’ proficiency in playing their instrument can translate into benefits when learning complex and refined motor skills in another domain. Even basic surgical skills, such as suturing, become difficult in cognitively demanding environments such as the operating room, containing a barrage of multisensory stimuli where the surgeon must triage and respond to clinically salient information. Method: Participants with piano expertise and participants with no formal music training learned how to do a surgical knot and sutures. They had two practice sessions and were tested after each session. The two test parameters measured were time to complete the task and an OSATS (Objective Structures Assessment of Technical Skills) score. Results for each group (musicians and non-musicians) were analysed and compared. Results: Musician participants performed the surgical tasks faster and received higher scores than the controls; for knot tying, the difference between the two groups was statistically significant. Gender and proficiency using chopsticks also exhibited some influence on test times and scores. Conclusion: Musical training in piano appeared to be of benefit in the initial stage of learning new simple surgical skills. This indicates that at least some aspects of a musicians’ skillset (such as fine motor control, bimanual dexterity and good hand-eye coordination) might be transferrable to an ostensibly disparate domain, and may be important for incorporation in surgical training where the skill of suturing can impact both surgical outcomes, patient safety, and patient satisfaction.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".