Defining Strategies of Modulation of Antiplatelet Therapy in Patients With Coronary Artery Disease: A Consensus Document from the Academic Research Consortium
Bibliographic record
Abstract
Antiplatelet therapy is the mainstay of pharmacologic treatment to prevent thrombotic or ischemic events in patients with coronary artery disease treated with percutaneous coronary intervention and those treated medically for an acute coronary syndrome. The use of antiplatelet therapy comes at the expense of an increased risk of bleeding complications. Defining the optimal intensity of platelet inhibition according to the clinical presentation of atherosclerotic cardiovascular disease and individual patient factors is a clinical challenge. Modulation of antiplatelet therapy is a medical action that is frequently performed to balance the risk of thrombotic or ischemic events and the risk of bleeding. This aim may be achieved by reducing (ie, de-escalation) or increasing (ie, escalation) the intensity of platelet inhibition by changing the type, dose, or number of antiplatelet drugs. Because de-escalation or escalation can be achieved in different ways, with a number of emerging approaches, confusion arises with terminologies that are often used interchangeably. To address this issue, this Academic Research Consortium collaboration provides an overview and definitions of different strategies of antiplatelet therapy modulation for patients with coronary artery disease, including but not limited to those undergoing percutaneous coronary intervention, and consensus statements on standardized definitions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".