Influence of diabetes and bilateral internal thoracic artery grafts on long-term outcome for multivessel coronary artery bypass grafting☆
Bibliographic record
Abstract
OBJECTIVE: Diabetes mellitus is a major independent risk factor for morbidity and mortality after coronary artery bypass grafting (CABG). The aim of this study was to assess the effect of bilateral (B) internal thoracic artery grafting (ITA) in diabetic patients with multivessel CABG. METHODS: Between 1985 and 1995, 4382 patients underwent primary isolated multivessel CABG with ITA grafting and concomitant saphenous vein grafting (SVG). Outcome of diabetic and nondiabetic patients undergoing single (S) ITA+SVG (n=419 and 2079) and BITA+SVG (n=214 and 1594) grafting was obtained at a mean follow-up of 11+/-3 years. RESULTS: Diabetic patients were older, included more women, and had more obesity, hypertension and peripheral vascular disease than nondiabetic patients. Deep sternal wound infection rate was 1.9% for diabetic patients vs 1.2% for nondiabetic patients (P=0.2) and 30-day mortality was 1.7 vs 1.8% (P=0.9). Cox regression analysis with interaction term and propensity scoring showed that BITA grafting decreased the risk of death (Hazard Ratio=0.72 [0.57-0.91, 95%CI]) and coronary reoperation (HR=0.38 [0.19-0.77]) in both diabetic and nondiabetic patients, with no significant interaction noted. BITA grafting decreased the risk of myocardial infarction at long-term follow-up in nondiabetic patients (HR=0.72 [0.60-0.86]) but not in diabetic patients. Ten-year freedom rate from myocardial infarction in diabetic patients was 80 and 76% for SITA and BITA grafting patients, respectively. However, survival following myocardial infarction was better for patients who underwent BITA grafting, in both diabetic and nondiabetic subgroups. CONCLUSIONS: BITA+SVG grafting in diabetic patients improves survival and decrease coronary reoperation compared with SITA+SVG at long-term follow-up. Survival following myocardial infarction is improved with BITA grafting.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".