Operating list composition and surgical performance
Bibliographic record
Abstract
BACKGROUND: Recent reviews suggest that the way in which surgeons prepare for a procedure (warm up) can affect performance. Operating lists present a natural experiment to explore this phenomenon. The aim was to use a routinely collected large data set on surgical procedures to understand the relationship between case list order and operative performance. METHOD: Theatre lists involving the 35 procedures performed most frequently by senior surgeons across 38 private hospitals in the UK over 26 months were examined. A linear mixed-effects model and matched analysis were used to estimate the impact of list order and the cost of switching between procedures on a list while controlling for key prognosticators. The influence of procedure method (open versus minimally invasive) and complexity was also explored. RESULTS: The linear mixed-effects model included 255 757 procedures, and the matched analysis 48 632 pairs of procedures. Repeating the same procedure in a list resulted in an overall time saving of 0·98 per cent for each increase in list position. Switching between procedures increased the duration by an average of 6·48 per cent. The overall reduction in operating time from completing the second procedure straight after the first was 6·18 per cent. This pattern of results was consistent across procedure method and complexity. CONCLUSION: There is a robust relationship between operating list composition and surgical performance (indexed by duration of operation). An evidence-based approach to structuring a theatre list could reduce the total operating time.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".