Impact of diabetes on 12-month outcomes following coronary artery bypass graft surgery: results from the ROSETTA-CABG Registry.
Bibliographic record
Abstract
BACKGROUND: Diabetes mellitus is associated with poorer long-term outcomes following coronary artery bypass graft (CABG) surgery. However, little is known about the impact of diabetes mellitus on outcomes during the first 12 months following CABG. OBJECTIVES: To examine the relationship between diabetes mellitus and outcomes during the 12 months following CABG. METHODS: The Routine versus Selective Exercise Treadmill Testing after Coronary Artery Bypass Grafting (ROSETTA-CABG) Registry is a prospective, multicentre study examining the use of functional testing after CABG surgery. A total of 398 patients who were enrolled in the ROSETTA-CABG Registry were examined. Diabetic status was defined by medication use at discharge. Only patients undergoing a first successful CABG (all ischemic areas thought to be revascularized) were included. RESULTS: Among the 398 patients, 37 (9.3%) were receiving insulin, 67 (16.8%) were receiving oral hypoglycemic agents, and 294 (73.9%) were not receiving insulin or oral hypoglycemic agents. Insulin-treated patients had a higher 12-month incidence of composite clinical events consisting of readmission for unstable angina, myocardial infarction or death than did oral hypoglycemic-treated patients and nondiabetic patients (21.6% versus 4.5% and 6.0%, respectively; P=0.0003). Insulin-treated patients were also more likely to undergo repeat cardiac catheterization than were oral hypoglycemic-treated patients and nondiabetic patients (18.9% versus 8.8% and 7.9%, respectively; P=0.03). After controlling for other variables, use of insulin was independently associated with a composite of adverse clinical events (OR 3.80, 95% CI 1.5 to 9.6, P=0.005). CONCLUSIONS: During the 12-month period after a successful CABG, insulin-treated patients had a higher rate of adverse cardiac events than did patients receiving oral hypoglycemic agents and nondiabetic patients. These results suggest that diabetic patients may benefit from more aggressive surveillance during the first year after CABG surgery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".