Ticagrelor Compared to Clopidogrel in aCute Coronary syndromes (TC4) – A Bayesian pragmatic cluster randomized controlled trial
Bibliographic record
Abstract
ABSTRACT Background Dual-antiplatelet therapy (DAPT) is the standard of care for acute coronary syndromes, but uncertainty exists regarding the optimal regime for North American patients. Methods This pragmatic, open-label, time clustered, randomized trial ( ClinicalTrials.Gov ( NCT04057300 ) compared the effectiveness and safety of DAPT with ticagrelor or clopidogrel in acute coronary syndrome patients from a single tertiary academic center in Montreal, Canada. The primary effectiveness endpoint was a composite of all-cause mortality, non-fatal myocardial infarction, or ischemic stroke. The primary safety endpoint were bleeding hospitalizations. Twelve-month outcomes were ascertained from the Québec universal electronic health databases. The study was designed and analyzed within a Bayesian paradigm to supplement existing knowledge. The primary analysis was a Bayesian logistic regression models with an informed focused prior from previously randomized North American patients. Robustness was evaluated with vague and other pre-specified informative priors, spanning reasonable pre-existing beliefs. Clinically significant benefits and harms were defined as risk reductions exceeding a 10% difference. Results 1,005 ACS patients were randomized to ticagrelor (n = 450) or clopidogrel (n = 555). MACE occurred in 50 (11.1%) ticagrelor and 64 (11.5%) clopidogrel patients (relative risk (RR), 0.95; 95% credible interval [95% CrI]: 0.67, 1.35 with a vague prior). The primary analysis with an informed focused prior resulted in probabilities of a clinically meaningful ticagrelor benefit (RR<0.9), equivalence (0.9 ≦ RR ≧, 1.1) or harm (RR ≧, 1.1) of 2%, 41% and 57%, respectively. For the safety endpoint, there was no consistent signal of benefit or harm with ticagrelor. Sensitivity analyses with a range of prior beliefs gave generally consistent results. Conclusions Whether this trial was analysed with a vague, or a range of reasonable informed priors, no strong evidence for the superiority of ticagrelor over clopidogrel was found.
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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".