Incomplete Revascularization After Coronary Artery Bypass Graft Operations Is Independently Associated With Worse Long-Term Survival
Bibliographic record
Abstract
BACKGROUND: Complete revascularization (CR) has been suggested to provide benefits to both early and long-term outcomes, but the magnitude of the benefit and frequency of incomplete revascularization (IR) after coronary artery bypass graft operations is rarely explored and is the subject of the present study. METHODS: All patients who underwent isolated bypass operations (March 1995 to September 2007) at the Queen Elizabeth II Health Sciences Center (Halifax, NS, Canada) were identified. Revascularization was considered complete if each significantly diseased territory received at least 1 graft. Clinical characteristics of the CR and IR groups were examined to determine barriers of CR. A nonparsimonious Cox proportion model and survival curves were constructed to examine the association of CR and death after adjusting for clinically relevant covariates. RESULTS: A total of 8,570 patients underwent isolated nonredo bypass operations. IR, based on our strict definition, occurred in 19% of the patients. The territories most commonly affected were the right coronary and circumflex coronary territories. After adjustment for relevant clinical differences, IR was a significant independent predictor of long-term mortality (hazard ratio, 1.2; 95% confidence interval, 1.1 to 1.3). IR was also a significant independent predictor of hospital readmission for cardiac reasons after discharge (hazard ratio, 1.2; 95% confidence interval, 1.0 to 1.3). CONCLUSIONS: Despite advances in surgical revascularization, IR can occur in up to 19% of patients. IR significantly affects long-term death and readmission to hospital for cardiac reasons, and avoiding IR should therefore be a priority for surgeons during preoperative planning.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".