The impact of diffuseness of coronary artery disease on the outcomes of patients undergoing primary and reoperative coronary artery bypass grafting☆
Bibliographic record
Abstract
OBJECTIVE: Diffuse coronary artery disease jeopardizes myocardium, increasing surgical mortality in primary coronary artery bypass grafting (CABG). We sought to determine the impact of diffuseness on pre- and post-discharge outcomes for both primary and reoperative CABG (REOP). METHODS: Using a validated system for measuring diffuseness of coronary disease, preoperative angiograms were scored for primary CABG (n=792) and REOP cases (n=268) performed 1997-2004. A diffuseness score (DS)>18 was defined as elevated. In-hospital mortality, intermediate-term survival, and in-hospital composite outcome (COMP) (one or more of: mortality, stroke, MI, deep sternal infection, sepsis, IABP insertion, or return to OR) were examined. RESULTS: In-hospital mortality and COMP for patients with DS>18 were significantly higher (7.9% vs 2.4%, p<0.0001), (17.8% vs 9.2%, p<0.0001). DS (mean+/-SD) was higher in REOP cases than primary CABG (18.9+/-7.1 vs 14.4+/-6.0, p<0.0001). By multivariate analysis, DS>18 (OR 2.00, 95%CI, 1.20-3.32, p=0.008) and REOP (OR 2.40, 95%CI, 1.53-3.77, p<0.0001) were independently associated with COMP. Using propensity scores 82% of cases with DS>18 (n=289) were matched 1:1 to cases with DS 18 (6.9% vs 2.8%, p=0.02), (16.6% vs 10.4%, p=0.03). Comparing cases with DS 18 and primary CABG versus REOP, survival at 2 years was 92.1% versus 84.5% (p=0.001) and 92.7% versus 82.7% (p<0.0001), respectively. CONCLUSIONS: Diffuse coronary artery disease is an important predictor of morbidity and mortality in primary and REOP CABG patients, and should be considered in both individual patient assessment and risk adjustment.
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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 | 0.003 |
| 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) | 0.001 | 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".