Do nonfatal events during the first 5 years after coronary artery bypass surgery influence 10 year outcomes?
Bibliographic record
Abstract
OBJECTIVE: Few have examined the influence of early adverse events after coronary artery bypass grafting (CABG) on long-term survival. We sought to determine if the occurrence of nonfatal major adverse cardiac and cerebrovascular events (MACCE) during the first 5 years after CABG influences survival and adverse events at 10 years. METHODS: All patients who underwent isolated CABG from 1990 to 2014 at a single center in Ontario, Canada, were included. Primary end point was all-cause mortality. The secondary end point of interest was MACCE, a composite of mortality, nonfatal myocardial infarction, stroke, and repeat revascularization. RESULTS: A total of 20,444 cases of elective primary isolated CABG were identified as being alive at 5 years, with 11% of patients developing nonfatal MACCE within the first 5 years after CABG (MACCE group) and the remaining 89% were alive without a MACCE event at 5 years (non-MACCE group). Following propensity score matching, 2167 patient-pairs were formed. Among the MACCE group, 972 out of 2167 (44.9%) developed a myocardial infarction, 519 out of 2167 (24.0%) had a stroke, and 946 out of 2167 (43.7%) required a repeat revascularization within the first 5 years after CABG. Non-MACCE was associated with better overall survival (hazard ratio, 1.42; 95% CI, 1.25-1.63; P < .01) and freedom from MACCE (hazard ratio, 1.61; 95% CI, 1.45-1.79; P < .01) up to 10 years after CABG compared with MACCE cases. CONCLUSIONS: Patients who experienced nonfatal MACCE during the first 5 years after CABG experienced worse survival and more MACCE at 10 years. Prevention of major adverse events during the first 5 years after surgical revascularization may be an important strategy to improve late outcomes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".