Association between day of the week of elective surgery and postoperative mortality
Bibliographic record
Abstract
BACKGROUND: In prior studies, higher mortality was observed among patients who had elective surgery on a Friday rather than earlier in the week. We investigated whether mortality after elective surgery was associated with day of the week of surgery in a Canadian population and whether the association was influenced by surgeon experience and volume. METHODS: We conducted a population-based retrospective cohort study in the province of Ontario, Canada. We included adults who underwent 1 of 12 elective daytime surgical procedures from Apr. 1, 2002, to Dec. 31, 2012. The primary outcome was 30-day mortality. We used generalized estimating equations to compare outcomes for surgeries performed on different days of the week, adjusting for patient and surgeon factors. RESULTS: A total of 402 899 procedures performed by 1691 surgeons met our inclusion criteria. The median length of hospital stay was 6 (interquartile range 5–8) days. Surgeon experience varied significantly by day of week (p < 0.001), with surgeons operating on Fridays having the least experience. Nearly all of the patients who had their procedure on a Friday had postoperative care on the weekend, as compared with 49.1% of those whose surgery was on a Monday (p < 0.001). We found no difference in the 30-day mortality between procedures performed on Fridays and those performed on Mondays (adjusted odds ratio 1.08, 95% confidence interval 0.97–1.21). INTERPRETATION: Although surgeon experience differed across days of the week, the risk of 30-day mortality after elective surgery was similar regardless of which day of the week the procedure took place.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".