Exploring a New Methodological Approach for Capturing the 'Slowing Down' Moments of Operative Practice
Bibliographic record
Abstract
The phenomenon of 'slowing down' in response to important cues in the operative field is proposed as a hallmark of expert surgical judgment. As part of a larger program of research, the purpose of this study was to explore a methodology for capturing 'slowing down' moments using a standardised task. Edited videos of 6 laparoscopic cholecystectomies were shown to 10 expert surgeons (>250 laparoscopic cholecystectomies completed). Participants were asked to think aloud while watching them as if observing each procedure in the operating room. Each session was audiotaped and transcribed. Many examples of 'slowing down' moments were identified in the transcripts, including several categories that were previously uncharacterised or undescribed. A subset of 'slowing down' moments was compared between participants. Many appeared to be inconsistent between expert surgeons, suggesting that with this methodology alone, formal teaching and assessment of the 'slowing down' phenomenon will be challenging.
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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 | 0.048 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".