Impact of platelet glycoprotein IIb/IIIa receptor inhibitors on outcomes of diabetic patients undergoing percutaneous coronary interventions using sirolimus‐eluting stents
Bibliographic record
Abstract
OBJECTIVE: We assessed the outcomes in diabetic patients undergoing percutaneous coronary intervention (PCI) using sirolimus-eluting stents (SES) as a function of treatment with glycoprotein (GP) IIb/IIIa inhibitors. METHODS AND RESULTS: Of 551 diabetic patients treated with a SES in nine trials (RAVEL, SIRIUS, E-SIRIUS, C-SIRIUS, REALITY, SVELTE, DIRECT, SIRIUS 2.25, and SIRIUS 4.0), 187 patients (33.9%) were administered GP IIb/IIIa inhibitors during PCI. GP IIb/IIIa blockade was associated with lower rates of myocardial infarction (MI) at 30 days (1.1% vs. 3.3%, P = 0.12) and at 1 year (1.1% vs. 4.7%, P = 0.04), and composite endpoint of cardiac death/MI at 1 year (2.2% vs. 6.2%, P = 0.05). Benefit from GP IIb/IIIa inhibitors was confined to 128 insulin-treated diabetics who had remarkable reduction in MI (0.0% vs. 6.3%, P = 0.04) and cardiac death/MI at 30 days (0.0% vs. 7.6%, P = 0.05) and at 1-year (0.0% vs. 13.4%, P = 0.01 and 0.0% vs. 15.7%, P = 0.0005, respectively). When treated with GP IIb/IIIa inhibitors, insulin-requiring diabetics had similar rates of 1-year death/MI when compared with the nondiabetic patients (0% vs. 4.7%, P = 0.13, respectively). There were no significant differences in outcomes as a function of GP IIb/IIIa blockade in diabetics not treated with insulin. CONCLUSION: In this analysis, outcomes of insulin requiring diabetic patients undergoing PCI with SES were considerably improved with adjunctive GP IIb/IIIa inhibitors by decreasing the rates of MI and composite endpoint of cardiac death/MI.
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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 | 0.001 |
| 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 | 0.000 |
| 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".