Randomized clinical trial to evaluate mental practice in enhancing advanced laparoscopic surgical performance
Bibliographic record
Abstract
BACKGROUND: Mental practice, the cognitive rehearsal of a task without physical movement, is known to enhance performance in sports and music. Investigation of this technique in surgery has been limited to basic operations. The purpose of this study was to develop mental practice scripts, and to assess their effect on advanced laparoscopic skills and surgeon stress levels in a crisis scenario. METHODS: Twenty senior surgical trainees were randomized to either conventional training or mental practice groups, the latter being trained by an expert performance psychologist. Participants' skills were assessed while performing a porcine laparoscopic jejunojejunostomy as part of a crisis scenario in a simulated operating room, using the Objective Structured Assessment of Technical Skill (OSATS) and bariatric OSATS (BOSATS) instruments. Objective and subjective stress parameters were measured, as well as non-technical skills using the Non-Technical Skills for Surgeons rating tool. RESULTS: An improvement in OSATS (P = 0.003) and BOSATS (P = 0.003) scores was seen in the mental practice group compared with the conventional training group. Seven of ten trainees improved their technical performance during the crisis scenario, whereas four of the ten conventionally trained participants deteriorated. Mental imagery ability improved significantly following mental practice training (P = 0.011), but not in the conventional group (P = 0.083). No differences in objective or subjective stress levels or non-technical skills were evident. CONCLUSION: Mental practice improves technical performance for advanced laparoscopic tasks in the simulated operating room, and allows trainees to maintain or improve their performance despite added stress.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".