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Record W2107367876 · doi:10.12927/hcq.2013.20875

ICES Report: New Findings Highlight Potential Risks of Common Drug Combination in Cardiac Patients

2009· article· en· W2107367876 on OpenAlexafffundabout
David N. Juurlink, Tara Gomes, Muhammad Mamdani

Bibliographic record

VenueHealthcare Quarterly · 2009
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsHealth Sciences CentreUniversity of TorontoSt. Michael's HospitalSunnybrook Health Science CentreInstitute for Clinical Evaluative Sciences
FundersCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term Care
KeywordsMedicineIntensive care medicineDrugBest practiceRisk analysis (engineering)PharmacologyPolitical science

Abstract

fetched live from OpenAlex

The Issue Clopidogrel (Plavix) is a commonly used medication in patients with cardiovascular disease. It works by interfering with the activity of platelets (blood elements responsible for clotting) and is often combined with acetylsalicylic acid (ASA) in patients who have had a myocardial infarction or stent. It is also used in selected patients with stroke. In 2007, clopidogrel was the second leading drug worldwide, with more than US$7.3 billion in sales, which is not surprising given the prevalence of cardiovascular and cerebrovascular diseases. Scientists have known for some time that clopidogrel is a prodrug – that is, it has no effect on platelets until it is converted by the liver into its active form. The liver’s ability to “turn on” clopidogrel is determined, in part, by genetics. Some patients (particularly those of Asian descent) lack the enzyme that converts clopidogrel to its active form. Recently, however, scientists have observed that some drugs can also interfere with this same process by blocking the primary liver enzyme responsible for this crucial step in the activation of clopidogrel. Of the drugs that could interfere with the activation of clopidogrel, by far the most important are the proton pump inhibitors (PPIs). These drugs are extremely popular, and millions of Canadians take one of these drugs for a variety of common acid-related disorders, including gastroesophageal reflux disease and peptic ulcer disease, and as a preventative measure against stomach injury from anti-inflammatory drugs such as ASA. Given the popularity of PPIs and the large number of patients treated with clopidogrel, it is likely that millions of patients take these drugs in combination worldwide. Some PPIs, but not others, interfere with the activity of the enzyme responsible for converting clopidogrel to its active form. Several laboratory-based studies have shown that certain PPIs appear to reduce the effect of clopidogrel in platelets, making blood “stickier” and more likely to clot. These findings support the notion that, in some patients, PPIs could reduce the effect of clopidogrel and increase the risk of cardiac events. However, until recently, no published studies characterized the clinical significance of the drug interaction between clopidogrel and PPIs. A recent study conducted at ICES and funded in part by the Ontario Ministry of Health and Long-Term Care provided the first large-scale insight into the clinical consequences of this extremely common drug interaction (Juurlink et al. 2009).

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0300.007

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 head0.016
GPT teacher head0.305
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2009
Admission routes3
Has abstractyes

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