International Expert Consensus on Switching Platelet P2Y <sub>12</sub> Receptor–Inhibiting Therapies
Bibliographic record
Abstract
Dual antiplatelet therapy with aspirin and a P2Y 12 inhibitor is the treatment of choice for the prevention of atherothrombotic events in patients with acute coronary syndromes and for those undergoing percutaneous coronary interventions. The availability of different oral P2Y 12 inhibitors (clopidogrel, prasugrel, ticagrelor) has enabled physicians to contemplate switching among therapies because of specific clinical scenarios. The recent introduction of an intravenous P2Y 12 inhibitor (cangrelor) further adds to the multitude of modalities and settings in which switching therapies may occur. In clinical practice, it is not uncommon to switch P2Y 12 inhibitor, and switching may be attributed to a variety of factors. However, concerns about the safety of switching between these agents have emerged. Practice guidelines have not fully elaborated on how to switch therapies, leaving clinicians with limited guidance on when and how to switch therapies when needed. This prompted the development of this expert consensus document by key leaders from North America and Europe with expertise in basic, translational, and clinical sciences in the field of antiplatelet therapy. This expert consensus provides an overview of the pharmacology of P2Y 12 inhibitors, different modalities and definitions of switching, and available literature and recommendations for switching between P2Y 12 inhibitors.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".