WTP6.14 The Effectiveness of Mental Practice in Surgical Skills Practice and Acquisition: A Review
Bibliographic record
Abstract
Abstract Aims Mental practice (MP) is an established method to enhance performance in other disciplines. The aim of this review is to assess whether the principles of MP are transferable to surgery and describe how it has been applied in our field so far. Methods A narrative review was conducted according to the four steps described by Demiris et. al. Results This review describes the different types of MP and their role on motor skill acquisition. The process of preparation, delivery and assessment of a MP session is described here in a reproducible manner; providing a ‘user guide’ to surgeons. MP has been shown in several reports to be an effective means in learning surgical skills, from basic surgical technique to the steps of a procedure. It has also been described as a technique applicable to both surgical trainees and established surgeons. Ten RCTs demonstrated a favourable effect of MP on technical skills and one trial showed that MP with MI is equivalent to additional physical practice. On the contrary two RCTs showed MP to be inferior or have no impact compared to other types of preparation. Reduced MP duration was identified as one of the possible reasons for a lack of positive results. Conclusion Mental practice is a feasible method for rehearsing surgical skills and procedures that has been demonstrated to enhance skills acquisition and surgical performance. Larger scale, randomised controlled trials are needed to standardise the duration, number of repetitions, and practical considerations for mental practice in surgery.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".