Fewer gastrointestinal bleeds with ticagrelor and prasugrel compared with clopidogrel in patients with acute coronary syndrome following percutaneous coronary intervention
Bibliographic record
Abstract
BACKGROUND: inhibitors lower major adverse cardiac events with similar or possibly higher major bleeding events. The comparative GIB rates of these medications remain poorly understood. AIM: To compare GIB rates associated with clopidogrel, prasugrel and ticagrelor using national medical and pharmacy claims data from privately insured and Medicare Advantage enrollees . METHODS: Propensity score and inverse probability treatment weighting were used to balance baseline characteristics among treatment groups. The 1-year GIB risk was calculated using weighted Cox proportional hazard models and expressed as hazard ratios (HR) with 95% confidence intervals (CI) and number needed to harm (NNH). RESULTS: We identified 37 019 patients with ACS (non-ST elevation ACS [NSTE-ACS] and ST-elevation myocardial infarction [STEMI]) within 14 days of a PCI (mean age 63 years and 70% male). Clopidogrel prescription was most common (69%) with prasugrel (16%) and ticagrelor (14%) prescribed less frequently. When compared with clopidogrel, ticagrelor was associated with a 34% risk reduction (HR 0.66; 95% CI: 0.54-0.81) in GIB overall and with NSTE-ACS, and a 37% GIB risk reduction (HR 0.63; 95% CI: 0.42-0.93) in STEMI patients. When compared with clopidogrel, prasugrel was associated with a 21% risk reduction (HR 0.79; 95% CI: 0.64-0.97) overall, a 36% GIB risk reduction (HR 0.64; 95% CI: 0.49-0.85) in STEMI patients but no reduction of GIB risk in NSTE-ACS patients. CONCLUSIONS: In the first year following PCI, ticagrelor or prasugrel are associated with fewer GIB events than clopidogrel.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".