Association between Complications and Death Within 30 days after General Surgery
Bibliographic record
Abstract
OBJECTIVE: To determine the epidemiology of postoperative complications among general surgery patients, inform their relationships with 30-day mortality, and determine the attributable fraction of death of each postoperative complication. BACKGROUND: The contemporary causes of postoperative mortality among general surgery patients are not well characterized. METHODS: VISION is a prospective cohort study of adult non-cardiac surgery patients across 28 centers in 14 countries who were followed for 30 days after surgery. For the subset of general surgery patients, a Cox proportional hazards model was used to determine associations between various surgical complications and postoperative mortality. The analyses were adjusted for preoperative and surgical variables. Results were reported in adjusted hazard ratios (HR) with 95% confidence intervals (CI). RESULTS: Among 7950 patients included in the study, 240 (3.0%) patients died within 30 days of surgery. Five postoperative complications [myocardial injury after non-cardiac surgery (MINS), major bleeding, sepsis, stroke, and acute kidney injury resulting in dialysis] were independently associated with death. Complications associated with the largest attributable fraction (AF) of postoperative mortality (ie, percentage of deaths in the cohort that can be attributed to each complication, if causality were established) were major bleeding (n=1454, 18.3%, HR 2.49 95% CI: 1.87-3.33, P <0.001, AF 21.2%), sepsis (n=783, 9.8%, HR 6.52, 95% CI: 4.72-9.01, P <0.001, AF 15.6%), and MINS (n=980, 12.3%, HR 2.00, 95% CI: 1.50-2.67, P <0.001, AF 14.4%). CONCLUSIONS: The complications most associated with 30-day mortality following general surgery are major bleeding, sepsis, and MINS. These findings may guide the development of mitigating strategies, including prophylaxis for perioperative bleeding.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".