Novel P2Y12 Receptor Antagonists - Prasugrel and Ticagrelor. Systematic Review, Indirect Comparison to Clopidogrel in Cardiovascular Disease, Design of a Randomized Controlled Trial
Bibliographic record
Abstract
Antiplatelet therapy with clopidogrel is widely used in patients with coronary artery disease, but the recent development of the new P2Y12 receptor antagonists prasugrel and ticagrelor will increase treatment options. An overview of systematic reviews was performed to summarize available evidence on clopidogrel. Current data on prasugrel and ticagrelor were identified by a systematic review and used for an indirect treatment comparison (ITC) of the drugs against each other and versus placebo in the absence of head-to-head clinical trials. Adjusted indirect comparison according to Bucher, Bayesian methods for mixed treatment comparisons using Winbugs, and generalized linear mixed models using SAS were employed for ITC, yielding almost identical results: prasugrel was favored regarding stent thrombosis and ticagrelor regarding major bleeding. However, substantial differences in trial design were identified, demanding caution when interpreting these results. On the basis of the obtained results, a randomized controlled trial was designed within the gap of current evidence.
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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".