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Record W2345567484 · doi:10.5539/gjhs.v9n1p89

Dual Antiplatelet Therapy after Coronary Artery Bypass Graft Surgery: A Review

2016· review· en· W2345567484 on OpenAlexvenueno aff
Hala Soomro, Salik Aleem, Ali M. Alam, Mohammad Ali Qadeer, Nabeeha Essam, Anas Ahmed Siddiqui, Muhammad Fasih Mansuri, Huda Fatima, Ali Raza, Ayyaz Sultan, Maha Begg, Maaz Khan, Muhammad Bazil Musharraf, Arbab Burhan, Muhammad Nawaz Lashari

Bibliographic record

VenueGlobal Journal of Health Science · 2016
Typereview
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAspirinClopidogrelMyocardial infarctionAnginaAcute coronary syndromeCardiologyInternal medicineUnstable anginaThrombosisSurgeryCoronary artery diseaseArteryComplicationPlatelet aggregation inhibitor

Abstract

fetched live from OpenAlex

Coronary artery bypass graft surgery (CABG) is the gold standard treatment for relieving angina symptoms and reducing mortality among ischemic heart disease patients. As post-operative thrombosis of the grafts has been a frequent complication of CABG, antiplatelet therapy remains essential to maintain graft patency. Since a long time, aspirin has been used as a single anti-platelet agent post CABG. However, in some high risk patients aspirin alone is insufficient in preventing graft occlusion. Therefore, dual antiplatelet therapy involving aspirin plus clopidogrel is becoming increasingly popular. Aspirin plus clopidogrel therapy has proved to be highly efficacious in patients with acute coronary syndrome; however, its role in patients after CABG has remained unclear. In this review, we outline the effects of dual antiplatelet therapy involving aspirin plus clopidogrel with respect to graft patency, post-operative angina/myocardial infarction, major bleeding event and mortality.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.048
GPT teacher head0.371
Teacher spread0.323 · 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 designSystematic review
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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Journal of Health Science→Same topicAntiplatelet Therapy and Cardiovascular Diseases→French-language works237,207→