Embedding mental rehearsal in surgery: a comprehensive review of the evidence
Bibliographic record
Abstract
Background: Mental rehearsal (MR), the deliberate practice of skills specific to a procedure, has been successfully used in sports and music training for decades, but has not been adopted in surgery. This narrative review explores MR's role in surgical training and clinical practice, evaluating its effectiveness in motor skill acquisition, technical and non-technical skill development, and real world clinical implementation. Our aim was to assess MR's impact on both surgical education and clinical performance, while identifying the barriers to its routine adoption in surgical training. Methods: We searched for relevant studies on the topic and impacts of MR in surgery using the Medline database up to December 2024. A range of studies were included covering mental rehearsal, surgical education, surgical training, and surgical outcomes. The primary outcomes were to provide insights into the mechanisms and implementation of MR in surgery and to assess the potential impact of MR on surgical outcomes. Results: The narrative review provides scientific insights into the mechanisms of MR in surgery and describes in detail the implementation methodology. The majority of evidence demonstrates that MR is beneficial when used as an adjunct approach to other forms of training. Moreover, there is evidence to support MR as a low-cost and valuable learning technique. Many questions remain regarding training schedules including the optimal duration and nature of the MR sessions, accommodating the surgeon's prior experience, optimal number of repetitions, and addressing the abilities of the participants to perform mental imagery. Most studies have heterogenous methods, diffuse aims and poor descriptions of the specific intervention components. Several studies applied MR in demanding real-life surgical environments and demonstrated feasibility in surgery. Conclusions: The preliminary findings suggest that MR may improve the performance of operators and operating teams as an efficient adjuvant to traditional surgical skills training methods. More work is needed to better understand how MR interventions can best be implemented to improve training, practice, and outcomes in routine surgical practice.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".