Bibliographic record
Abstract
Despite improvements in the treatment of acute coronary syndromes, cardiovascular disease remains the leading cause of death in Europe and the United States. Antiplatelet agents, such as aspirin and clopidogrel, play an important role in the treatment of those patients. Several new alternatives have been tested in clinical trails and some of them have been approved for routine treatment of patients with ACS in Switzerland and the European Union. The latter include Prasugrel (Efient®) and Ticagrelor (Brilique®). Those substances provide more rapid and consistent platelet inhibition but increase the risk of bleeding in some patient subgroups. Thus, the main challenge is to tailor treatment for each patient by taking into consideration patient characteristics, comorbidities, underlying short- and long-term risk factors, ischemic and bleeding risks, and expected individual responses to different medications. This ambitious new approach will be a challenge for in daily clinical work and may ultimately require prioritization among several treatment alternatives. In this article, we review the new antiplatelet agents being developed as well as their pharmacological characteristics, key interactions and side effects and potential clinical indications in subpopulations.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".