Clinical evidence of interaction between clopidogrel and proton pump inhibitors
Bibliographic record
Abstract
Clopidogrel is approved for reduction of atherothrombotic events in patients with cardiovascular (CV) and cerebrovascular disease. Dual antiplatelet therapy with aspirin and clopidogrel decreases the risk of major adverse cardiac events after acute coronary syndrome or percutaneous coronary intervention, compared with aspirin alone. Due to concern about gastrointestinal bleeding in patients who are receiving clopidogrel and aspirin therapy, current guidelines recommend combined use of a proton pump inhibitor (PPI) to decrease the risk of bleeding. Data from previous pharmacological studies have shown that PPIs, which are extensively metabolized by the cytochrome system, may decrease the ADP-induced platelet aggregation of clopidogrel. Results from retrospective cohort studies have shown a higher incidence of major CV events in patients receiving both clopidogrel and PPIs than in those without PPIs. However, other retrospective analyses of randomized clinical trials have not shown that the concomitant PPI administration is associated with increased CV events among clopidogrel users. These controversial results suggest that large specific studies are needed. This article reviews the metabolism of clopidogrel and PPIs, existing clinical data regarding the interaction between clopidogrel and PPIs, and tries to provide recommendations for health care professionals.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".