Platelet Glycoprotein IIb/IIIa Inhibitors Reduce Mortality in Diabetic Patients With Non–ST-Segment-Elevation Acute Coronary Syndromes
Bibliographic record
Abstract
BACKGROUND: Diabetes mellitus is a major risk factor for adverse outcomes after acute coronary syndromes (ACS). Because this disease may be associated with increased platelet aggregation, we investigated whether diabetic patients with ACS derive particular benefit from platelet glycoprotein (GP) IIb/IIIa receptor inhibition. METHODS AND RESULTS: We performed a meta-analysis of the diabetic populations enrolled in the 6 large-scale platelet GP IIb/IIIa inhibitor ACS trials: PRISM, PRISM-PLUS, PARAGON A, PARAGON B, PURSUIT, and GUSTO IV. Among 6458 diabetic patients, platelet GP IIb/IIIa inhibition was associated with a significant mortality reduction at 30 days, from 6.2% to 4.6% (OR 0.74; 95% CI 0.59 to 0.92; P=0.007). Conversely, 23 072 nondiabetic patients had no survival benefit (3.0% versus 3.0%). The interaction between platelet GP IIb/IIIa inhibition and diabetic status was statistically significant (P=0.036). Among 1279 diabetic patients undergoing percutaneous coronary intervention (PCI) during index hospitalization, the use of these agents was associated with a mortality reduction at 30 days from 4.0% to 1.2% (OR 0.30; 95% CI 0.14 to 0.69; P=0.002). CONCLUSIONS: This meta-analysis, including the entire large-scale trial experience of intravenous platelet GP IIb/IIIa inhibitors for the medical management of non-ST-segment-elevation ACS, shows that these agents may significantly reduce mortality at 30 days in diabetic patients. Although not based on a randomized assessment, the survival benefit appears to be of greater magnitude in patients undergoing PCI. Therefore, the use of platelet GP IIb/IIIa inhibitors should be strongly considered in diabetic patients with ACS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".