Delay in admission for elective coronary-artery bypass grafting is associated with increased in-hospital mortality
Bibliographic record
Abstract
BACKGROUND: Many health care systems now use priority wait lists for scheduling elective coronary artery bypass grafting (CABG) surgery, but there have not yet been any direct estimates of reductions in in-hospital mortality rate afforded by ensuring that the operation is performed within recommended time periods. METHODS: We used a population-based registry to identify patients with established coronary artery disease who underwent isolated CABG in British Columbia, Canada. We studied whether postoperative survival during hospital admission for CABG differed significantly among patients who waited for surgery longer than the recommended time, 6 weeks for patients needing semi-urgent surgery and 12 weeks for those needing non-urgent surgery. RESULTS: Among 7316 patients who underwent CABG, 97 died during the same hospital admission, for a province-wide death rate at discharge of 1.3%. The observed proportion of patients who died during the same admission was 1.0% (27 deaths among 2675 patients) for patients treated within the recommended time and 1.5% (70 among 4641) for whom CABG was delayed. After adjustment for age, sex, anatomy, comorbidity, calendar period, hospital, and mode of admission, patients with early CABG were only 2/3 as likely as those for whom CABG was delayed to experience in-hospital death (odds ratio 0.61; 95% confidence interval [CI] 0.39 to 0.96). There was a linear trend of 5% increase in the odds of in-hospital death for every additional month of delay before surgery, adjusted OR = 1.05 (95% CI 1.00 to 1.11). CONCLUSION: We found a significant survival benefit from performing surgical revascularization within the time deemed acceptable to consultant surgeons for patients requiring the treatment on a semi-urgent or non-urgent basis.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".