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Record W2919041175 · doi:10.1161/circresaha.118.313141

Synergy of Dual Pathway Inhibition in Chronic Cardiovascular Disease

2019· article· en· W2919041175 on OpenAlexaff
Michiel Coppens, Jeffrey I. Weitz, John W.A. Eikelboom

Bibliographic record

VenueCirculation Research · 2019
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsPopulation Health Research InstituteMcMaster UniversityThrombosis and Atherosclerosis Research Institute
FundersDaiichi Sankyo EuropeServierPfizer
KeywordsMedicineRivaroxabanAntithromboticAspirinAdverse effectWarfarinCoronary artery diseaseInternal medicineClopidogrelPharmacologyCardiologyAtrial fibrillation

Abstract

fetched live from OpenAlex

Although acetylsalicylic acid is of proven benefit for secondary prevention in patients with cardiovascular disease, the risk of recurrent ischemic events remains high. Intensification of antithrombotic therapy with more potent antiplatelet drugs, dual antiplatelet therapy, or vitamin K antagonists further reduces the risk of major adverse cardiovascular events compared with acetylsalicylic acid alone but increases the risk of bleeding without reducing mortality. In patients with prior coronary artery disease or peripheral arterial disease the COMPASS (Cardiovascular Outcomes for People Using Anticoagulation Strategies) trial revealed that compared with acetylsalicylic acid alone, dual pathway inhibition with low-dose rivaroxaban (2.5 mg twice-daily), an oral factor Xa inhibitor, plus acetylsalicylic acid reduced major adverse cardiovascular event by 24%, major adverse limb events by 47%, and mortality by 18%. Major bleeding was increased by 70%, but there was no increase in fatal or intracranial bleeding. This article (1) reviews the results of the COMPASS trial, (2) explains why dual pathway inhibition is superior to antiplatelet or anticoagulant therapy alone, (3) compares the results with rivaroxaban plus aspirin with those with other antithrombotic regimens, and (4) provides insight into how best to apply the COMPASS results into practice.

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)0.001
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 score0.508
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.042
GPT teacher head0.312
Teacher spread0.270 · 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

Citations41
Published2019
Admission routes1
Has abstractyes

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