Do Longer Delays for Coronary Artery Bypass Surgery Contribute to Preoperative Mortality in Less Urgent Patients?
Bibliographic record
Abstract
BACKGROUND: Priority wait lists are common for managing access to cardiac surgery in publicly funded health systems. We evaluated whether longer delays contribute to the probability of death before surgery among patients prioritized into the less urgent category. METHODS: We studied records of 9233 patients registered for isolated coronary artery bypass graft (CABG) in British Columbia, Canada. The primary outcome was death before surgery. We estimated the probability that a patient, who could be removed from the list as a result of surgery, death, or other competing events, dies on or before a certain wait-list week. RESULTS: Despite similar death rates in semiurgent and nonurgent groups, 0.63 (95% confidence interval, 0.46-0.80) versus 0.58 (0.36-0.80) per 1000 patient-weeks, nonurgent patients were remaining on the list longer, which contributed to higher cumulative incidence of all-cause death than in semiurgent group (adjusted odds ratio = 1.66; 1.03-2.68). By 52 weeks on the wait list, 0.9% (0.6-1.1) and 1.3% (0.8-1.8) of patients died in semiurgent and nonurgent groups, respectively (P < 0.01). Similar proportions of deaths related to cardiovascular disease estimated over wait-list time in both groups (P = 0.40) were the result of shorter delays in the semiurgent group despite a higher rate of death resulting from cardiovascular disease (0.50 [0.36-0.65] vs. 0.34 [0.17-0.51] per 1000 patient-weeks). CONCLUSION: Queuing according to urgency of treatment contributed to a higher proportion of CABG candidates dying before surgery from all causes in the nonurgent compared with the semiurgent group despite similar weekly death rates observed in both groups. However, similar probabilities of death resulting from cardiovascular disease observed in both groups over wait-list time were the result of shorter delays in the semiurgent group despite a higher rate of cardiovascular death.
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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 | 0.015 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".