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Record W2079442065 · doi:10.4330/wjc.v3.i5.153

Clinical evidence of interaction between clopidogrel and proton pump inhibitors

2011· article· en· W2079442065 on OpenAlexfundno aff
Shoa-Lin Lin

Bibliographic record

VenueWorld Journal of Cardiology · 2011
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsnot available
FundersCanadian Cardiovascular SocietyAmerican Heart Association
KeywordsClopidogrelMedicineAspirinAcute coronary syndromePercutaneous coronary interventionInternal medicineProton-pump inhibitorPlatelet aggregation inhibitorAdverse effectCardiologyMyocardial infarctionIntensive care medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.094
GPT teacher head0.364
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2011
Admission routes1
Has abstractyes

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