Teaching surgery takes time: the impact of surgical education on time in the operating room
Bibliographic record
Abstract
BACKGROUND: It is generally accepted that surgical training is associated with increased surgical duration. The purpose of this study was to determine the magnitude of this increase for common surgical procedures by comparing surgery duration in teaching and nonteaching hospitals. METHODS: This retrospective population-based cohort study included all adult residents of Ontario, Canada, who underwent 1 of 14 surgical procedures between 2002 and 2012. We used several linked administrative databases to identify the study cohort in addition to patient-, surgeon- and procedure-related variables. We determined surgery duration using anesthesiology billing records. Negative binomial regression was used to model the association between teaching versus nonteaching hospital status and surgery duration. RESULTS: Of the 713 573 surgical cases included in this study, 20.8% were performed in a teaching hospital. For each procedure, the mean surgery duration was significantly longer for teaching hospitals, with differences ranging from 5 to 62 minutes across individual procedures in unadjusted analyses (all p < 0.001). In regression analysis, procedures performed in teaching hospitals were associated with an overall 22% (95% confidence interval 20%-24%) increase in surgery duration, adjusting for patient-, surgeon- and procedure-related variables as well as the clustering of patients within surgeons and hospitals. CONCLUSION: Our results show that a wide range of surgical procedures require significantly more time to perform in teaching than nonteaching hospitals. Given the magnitude of this difference, the impact of surgical training on health care costs and clinical outcomes should be a priority for future studies.
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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 | 0.001 | 0.009 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".