De-escalation of antiplatelet therapy in patients with coronary artery disease: Time to change our strategy?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dual antiplatelet therapy (DAPT) is the gold standard after acute coronary syndromes (ACS) or chronic coronary syndromes (CCS) undergoing percutaneous coronary intervention (PCI). Because local and systemic ischemic complications can occur particularly in the early phase (i.e. 1–3 months) after ACS or PCI, the synergistic platelet inhibition of aspirin and a P2Y 12 inhibitor is of the utmost importance in this early phase. Moreover, the use of the more potent P2Y 12 inhibitors prasugrel and ticagrelor have shown to further reduce the incidence of ischemic events compared to clopidogrel after an ACS. On the other hand, prolonged and potent antiplatelet therapy are inevitably associated with increased bleeding, which unlike thrombotic risk, tends to be stable over time and may outweigh the benefit of reducing ischemic events in these patients. The duration and composition of antiplatelet therapy remains a topic of debate in cardiology due to competing ischemic and bleeding risks, with guidelines and recommendations considerably evolving in the past years. An emerging strategy, called "de-escalation", consisting in the administration of a less intense antithrombotic therapy after a short course of standard DAPT, has shown to reduce bleeding without any trade-off in ischemic events. De-escalation may be achieved with different antithrombotic strategies and can be either unguided or guided by platelet function or genetic testing. The aim of this review is to summarize the evidence and provide practical recommendations on the use of different de-escalation strategies in patients with ACS and CCS.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it