Weekend Surgical Care and Postoperative Mortality
Bibliographic record
Abstract
BACKGROUND: An association between weekend health care delivery and poor outcomes has become known as the "weekend effect." Evidence for such an association among surgery patients has not previously been synthesized. OBJECTIVE: To systematically review associations between weekend surgical care and postoperative mortality. METHODS: We searched PubMed, EMBASE, and references of relevant articles for studies that compared postoperative mortality either; (1) according to the day of the week of surgery for elective operations, or (2) according to weekend versus weekday admission for urgent/emergent operations. Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) for postoperative mortality (≤90 d or inpatient mortality) were pooled using random-effects models. RESULTS: Among 4027 citations identified, 10 elective surgery studies and 19 urgent/emergent surgery studies with a total of >6,685,970 and >1,424,316 patients, respectively, met the inclusion criteria. Pooled odds of mortality following elective surgery rose in a graded manner as the day of the week of surgery approached the weekend [Monday OR=1 (reference); Tuesday OR=1.04 (95% CI=0.97-1.11); Wednesday OR=1.08 (95% CI=0.98-1.19); Thursday OR=1.12 (95% CI=1.03-1.22); Friday OR=1.24 (95% CI=1.10-1.38)]. Mortality was also higher among patients who underwent urgent/emergent surgery after admission on the weekend relative to admission on weekdays (OR=1.27; 95% CI=1.08-1.49). CONCLUSIONS: Postoperative mortality rises as the day of the week of elective surgery approaches the weekend, and is higher after admission for urgent/emergent surgery on the weekend compared with weekdays. Future research should focus on clarifying underlying causes of this association and potentially mitigating its impact.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".