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Record W2468077222 · doi:10.2217/bmm-2016-0070

Platelet Function Testing As a Biomarker for Efficacy of Antiplatelet Drugs

2016· review· en· W2468077222 on OpenAlexaff
Marie Lordkipanidzé, Derek So, Jean‐François Tanguay

Bibliographic record

VenueBiomarkers in Medicine · 2016
Typereview
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsUniversité de MontréalUniversity of OttawaMontreal Heart Institute
Fundersnot available
KeywordsMedicineClopidogrelAspirinAntiplatelet drugPlateletTicagrelorPrasugrelIntensive care medicineBiomarkerInternal medicineDrugCardiologyPharmacology

Abstract

fetched live from OpenAlex

Despite the overwhelming evidence in support of the efficacy of dual antiplatelet therapy with aspirin and clopidogrel, it is also obvious that not all patients benefit from these drugs to the same extent. This interindividual variability in platelet responses may underlie clinical differences in drug efficacy, with potential for optimization of antiplatelet therapy to prevent ischemic events without excessively increasing bleeding risk. This review presents the current evidence regarding platelet function testing for monitoring of antiplatelet therapy, with emphasis on the prognostic value of platelet function testing to predict ischemic and bleeding events. The potential of platelet function testing to provide personalized antiplatelet therapy is also discussed, with an outlook toward the future of platelet function testing in high-risk individuals.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.069
GPT teacher head0.364
Teacher spread0.294 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations12
Published2016
Admission routes1
Has abstractyes

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