2200Adherence to cardiac surgical waitlist guidelines is a poor predictor of cardiac surgery waitlist mortality
Bibliographic record
Abstract
Background: Cardiac surgery waitlist recommendations which were developed based on expert opinion and structural information poorly predict pre-operative cardiac events. Studies reporting waitlist mortality risk factors are limited by unreported model performance and have not evaluated risk associated with non-adherence to waitlist recommendations. Methods: In a population-based dataset we identified 12,464 patients referred for cardiac surgery from 2009–2015. Logistic regression was used to identify independent predictors of cardiac surgical waitlist mortality. Model discrimination was assessed with a c-index and calibration with the Hosmer-Lemeshow test. Results: A total of 101 (0.8%) patients died awaiting cardiac surgery. The median wait-times and frequency of waitlist deaths among patients undergoing emergent, urgent, semi-urgent, and non-urgent surgery were 0.6, 7.3, 69.0, 56.3 days (p<0.001) and 6.3%, 0.8%, 0.3%, 0.5% (p<0.001) respectively. Non-adherence to waitlist recommendations trended higher among waitlist deaths (53.5% vs 47.2%, p=0.207) and was a poor predictor of waitlist mortality (odds ratio 1.29, 95% CI 0.87–1.91; c-index=0.530). After multivariable adjustment, 10 variables (Table) were independently associated with waitlist mortality (c-index=0.854, Hosmer-Lemeshow p=0.788, Figure).
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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 | 0.002 | 0.010 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".