Comprehensive Surgical Coaching Enhances Surgical Skill in the Operating Room
Bibliographic record
Abstract
In Brief Objectives: The aim of the study was to determine whether individualized coaching improved surgical technical skill in the operating room to a higher degree than current residency training. Background: Clinical training in the operating room is a valuable opportunity for surgeons to acquire skill and knowledge; however, it often remains underutilized. Coaching has been successfully used in various industries to enhance performance, but its role in surgery has been insufficiently investigated. Methods: This randomized controlled trial was conducted at one surgical training program. Trainees undergoing a minimally invasive surgery rotation were randomized to either conventional training (CT) or comprehensive surgical coaching (CSC). CT included ward and operating room duties, and regular departmental teaching sessions. CSC comprised performance analysis, debriefing, feedback, and behavior modeling. Primary outcome measures were technical performance as measured on global and procedure-specific rating scales, and surgical safety parameters, measured by error count. Operative performance was assessed by blinded video analysis of the first and last cases recorded by the participants during their rotation. Results: Twenty residents were randomized and 18 completed the study. At posttraining the CSC group (n = 9) scored significantly higher on a procedure-specific skill scale compared with the CT group (n = 9) [median, 3.90 (interquartile range, 3.68–4.30) vs 3.60 (2.98–3.70), P = 0.017], and made fewer technical errors [10 (7–13) vs 18 (13–21), P = 0.003]. Significant within-group improvements for all skill metrics were only noted in the CSC group. Conclusions: Comprehensive surgical coaching enhances surgical training and results in skill acquisition superior to conventional training. This randomized controlled trial examined the effectiveness of comprehensive coaching as an approach to enhance operative performance in surgical postgraduate training. Coaching involved structured performance analysis, video debriefing, feedback, and behavior modeling. After 2 months, coaching led to superior surgical skills as measured by performance and error metrics compared with conventional residency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".