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Record W3214144683 · doi:10.5772/intechopen.96328

Dual Antiplatelet Therapy after PCI: When Could We Go Shorter?

2021· book-chapter· en· W3214144683 on OpenAlexaff
Marcel Santaló-Corcoy, Guillaume Marquis‐Gravel, Jean‐François Tanguay

Bibliographic record

VenueIntechOpen eBooks · 2021
Typebook-chapter
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsConventional PCIMedicinePercutaneous coronary interventionDuration (music)Internal medicineCardiologyStentIntensive care medicineExpert opinionMyocardial infarction

Abstract

fetched live from OpenAlex

The optimal duration of dual antiplatelet therapy (DAPT) after percutaneous coronary intervention (PCI) remains an important clinical question in interventional cardiology. Several clinical and angiographic variables are associated with an increased risk for thrombotic events, and prolonged DAPT duration may improve long term clinical outcome. However, some patients also present high bleeding risk (HBR) characteristics and may require a shorter DAPT duration. The guidelines recommendations consider the data from randomized clinical trials, however numerous exclusion criteria may create gaps in the evidence leading to uncertainties, the need for expert opinion and patient level decision making. Furthermore, the stent platforms have evolved in such way that opportunities now exist to shorten duration of DAPT. This chapter will review the variables associated with ischemic and bleeding risks as well as different stent platforms to help clinicians optimize DAPT duration in patients undergoing PCI.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0180.011

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 head0.029
GPT teacher head0.256
Teacher spread0.227 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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