Operative length independently affected by surgical team size: data from 2 Canadian hospitals
Bibliographic record
Abstract
BACKGROUND: Knowledge of the composition of a surgical team is the premise for studying efficiency inside the operating room. METHODS: To investigate the team composition in general surgery procedures, we retrospectively reviewed procedures performed by an expert general surgeon in 2007-08 at 2 tertiary hospitals. For each patient, demographic characteristics, procedure type, team members and procedure length were extracted from intraoperative nursing records. We assessed procedure complexity using a calculated index. Multiple logistic regressions were performed to assess the association between procedure length and team size after adjusting for procedure complexity and patient condition. RESULTS: For the 587 procedures reviewed, the mean procedure length was 88 (standard deviation [SD] 51) minutes. On average, 8 team members (range 4-14), including surgeons, anesthesiologists, nurses and other specialists, were involved in each procedure. Only 47 (8%) procedures were performed by 1 surgeon. Most were performed by 2 (295 [50%]) or 3 surgeons (214 [36%]). Half the team members were nurses (mean 4, range 1-7). Both the complexity of the operation and the team size affected the procedure length significantly. When procedure complexity and patient condition were constant, adding 1 team member predicted a 7-minute increase in procedure length. CONCLUSION: This study demonstrates that a frequent change of core team members has a negative impact on surgical performance. Management strategies need to improve to optimize team efficiency in the operating room.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".