Benchmarking the vital risk of waiting for coronary artery bypass surgery in Ontario.
Bibliographic record
Abstract
BACKGROUND: Deaths among patients awaiting coronary artery bypass grafting (CABG) are a source of private grief and public concern in Canada. However, some deaths are expected over time among patients with coronary artery disease. Methods of benchmarking the burden of delayed care may be useful in understanding and managing waiting lists for CABG and other health services. The authors therefore determined the vital risk among people waiting for CABG in Ontario and compared it with the risk in the general population and among people living with coronary artery disease. METHODS: Patients registered to undergo CABG in Ontario between 1991 and 1995 were followed to ascertain numbers and dates of preoperative deaths or completed operations. Linking hospital discharge abstract data to vital statistics for 1991 to 1994, the authors defined a cohort of people who had survived 6 months after an acute myocardial infarction (AMI) and followed them for an additional 6 months to determine numbers and dates of deaths. They matched patients by age and sex and then calculated the standardized mortality ratio for each cohort (i.e., the ratio of observed deaths to those expected based on age- and sex-specific daily probabilities of death for the provincial population). RESULTS: Among 21,220 patients awaiting CABG, there were 82 preoperative deaths over a median follow-up of 18 days; the standardized mortality ratio was 2.92 (95% confidence limit [CL] 2.29-3.55). Among 21,220 matched 6-month survivors of an AMI, there were 663 deaths over a median follow up of 185 days; the standardized mortality ratio was 3.84 (95% CI 3.54-4.14). INTERPRETATION: Patients awaiting CABG in Ontario are at a much greater risk of death than the general population. However, when compared with thousands of other patients living with coronary artery disease, they are at similar or decreased vital risk.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".