Impact of Completeness of Revascularization on Long-Term Cardiovascular Outcomes in Patients With Type 2 Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: Patients with diabetes have more extensive coronary disease than those without diabetes, resulting in more challenging percutaneous coronary intervention or surgical (coronary artery bypass graft) revascularization and more residual jeopardized myocardium. The Bypass Angioplasty Revascularization Investigation 2 Diabetes (BARI 2D) trial provided an opportunity to examine the long-term clinical impact of completeness of revascularization in patients with diabetes. METHODS AND RESULTS: This is a post hoc, nonrandomized analysis of the completeness of revascularization in 751 patients who were randomly assigned to early revascularization, of whom 264 underwent coronary artery bypass graft surgery and 487 underwent percutaneous coronary intervention. The completeness of revascularization was determined by the residual postprocedure myocardial jeopardy index (RMJI). RMJI is a ratio of the number of myocardial territories supplied by a significantly diseased epicardial coronary artery or branch that was not successfully revascularized, divided by the total number of myocardial territories. Mean follow-up for mortality was 5.3 years. Complete revascularization (RMJI=0) was achieved in 37.9% of patients, mildly incomplete revascularization (RMJI >0≤33) in 46.6%, and moderately to severely incomplete revascularization (RMJI >33) in 15.4%. Adjusted event-free survival was higher in patients with more complete revascularization (hazard ratio, 1.14; P=0.0018). CONCLUSIONS: Patients with type 2 diabetes mellitus and less complete revascularization had more long-term cardiovascular events. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00006305.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".