Risk Factors for Hospital Readmission Following Noncardiac Surgery: International Cohort Study
Why this work is in the frame
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Bibliographic record
Abstract
Objective: To determine timing and risk factors associated with readmission within 30 days of discharge following noncardiac surgery. Background: Hospital readmission after noncardiac surgery is costly. Data on the drivers of readmission have largely been derived from single-center studies focused on a single surgical procedure with uncertainty regarding generalizability. Methods: We undertook an international (28 centers, 14 countries) prospective cohort study of a representative sample of adults ≥45 years of age who underwent noncardiac surgery. Risk factors for readmission were assessed using Cox regression (ClinicalTrials.gov, NCT00512109). Results: Of 36,657 eligible participants, 2744 (7.5%; 95% confidence interval [CI], 7.2-7.8) were readmitted within 30 days of discharge. Rates of readmission were highest in the first 7 days after discharge and declined over the follow-up period. Multivariable analyses demonstrated that 9 baseline characteristics (eg, cancer treatment in past 6 months; adjusted hazard ratio [HR], 1.44; 95% CI, 1.30-1.59), 5 baseline laboratory and physical measures (eg, estimated glomerular filtration rate or on dialysis; HR, 1.47; 95% CI, 1.24-1.75), 7 surgery types (eg, general surgery; HR, 1.86; 95% CI, 1.61-2.16), 5 index hospitalization events (eg, stroke; HR, 2.21; 95% CI, 1.24-3.94), and 3 other factors (eg, discharge to nursing home; HR, 1.61; 95% CI, 1.33-1.95) were associated with readmission. Conclusions: Readmission following noncardiac surgery is common (1 in 13 patients). We identified perioperative risk factors associated with 30-day readmission that can help frontline clinicians identify which patients are at the highest risk of readmission and target them for preventive measures.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it