Coronary Bypass Graft Patency in Patients With Diabetes in the Bypass Angioplasty Revascularization Investigation (BARI)
Bibliographic record
Abstract
BACKGROUND: Few studies have compared long-term status of bypass grafts between patients with and without diabetes, and uncertainty exists as to whether diabetes independently predicts poor clinical outcome after CABG. METHODS AND RESULTS: Among 1526 patients in BARI who underwent CABG as initial revascularization, 99 of 292 (34%) with treated diabetes mellitus (TDM) (those on insulin or oral hypoglycemic agents) and 469 of 1234 (38%) without TDM had follow-up angiography. Angiograms with the longest interval from initial surgery and before any percutaneous graft intervention (mean 3.9 years) were reviewed. An average of 3.0 grafts were placed at initial CABG for patients with TDM (n=297; internal mammary artery [IMA], 33%) and 2.9 grafts for patients without TDM (n=1347; IMA, 34%). Patients with TDM were more likely than those without to have small (<1.5 mm) grafted distal vessels (29% versus 22%) and vessels of poor quality (9% versus 6%). On follow-up angiography, 89% of IMA grafts were free of stenoses > or =50% among patients with TDM versus 85% among patients without TDM (P=0.23). For vein grafts, the corresponding percentages were 71% versus 75% (P=0.40). After statistical adjustment, TDM was unrelated to having a graft stenosis > or =50% (adjusted odds ratio, 0.87; 95% CI, 0.58 to 1.32). CONCLUSIONS: Despite diabetic patients' having smaller distal vessels and vessels judged to be of poorer quality, diabetes does not appear to adversely affect patency of IMA or vein grafts over an average of 4-year follow-up. Previously observed differences in survival between CABG-treated patients with and without diabetes may be largely a result of differential risk of mortality from noncardiac causes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".