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REVERSAL OF THE ANTI-PLATELET EFFECTS OF ASPIRIN AND CLOPIDOGREL

2012· article· en· W2076238851 on OpenAlexaff
Chunjian Li, John W. Eikelboom

Bibliographic record

VenueHeart · 2012
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsMcMaster University
Fundersnot available
KeywordsClopidogrelAspirinMedicinePlateletCohortInternal medicineAnesthesiaTiclopidineBlood samplingGastroenterologyCardiology

Abstract

fetched live from OpenAlex

Objectives Guidelines recommend stopping aspirin and clopidogrel 7–10 days before surgery to allow time for replacement of permanently inhibited platelets by newly released uninhibited platelets. The purpose of this study was to determine the rate of offset of the anti-platelet effects of aspirin and clopidogrel after stopping treatment and the proportion of untreated donor platelets that are required to reverse their anti-platelet effects. Methods Cohort 1 consisted of 15 healthy subjects who received aspirin 81 mg/d or clopidogrel 75 mg/d for 7 days and underwent serial blood sampling until platelet function testing results normalised. Cohort 2 consisted of 36 healthy subjects who received aspirin 325 mg/d, clopidogrel 75 mg/d, aspirin 81 mg/d plus clopidogrel 75 mg/d or no treatment for 7 days and underwent a single blood sampling. Results In cohort 1, Arachidonic acid (AA)-induced light transmission aggregation (LTA) returned to baseline levels in all subjects within 4 days of stopping aspirin, coinciding with partial recovery of plasma thromboxane B 2 concentrations. ADP-induced LTA did not return to baseline levels until 10 days after stopping clopidogrel. In cohort 2, AA-induced LTA in patient treated with aspirin reached control levels after mixing with 30% untreated donor platelets whereas ADP-induced LTA in patients treated with clopidogrel reached control levels only after the addition of 90% or more donor platelets. Conclusions Platelet aggregation recovers within 4 days of stopping aspirin but clopidogrel must be stopped for 10 days to achieve a normal aggregatory response.

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)0.000
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 score0.096
Threshold uncertainty score0.151

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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