Comparison of Clopidogrel Versus Ticagrelor for Percutaneous Coronary Intervention (PCI) Patients Managed with Therapeutic Hypothermia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Various antiplatelet drugs, such as clopidogrel and ticagrelor, are available on the market for use after percutaneous coronary intervention (PCI). However, the efficacy of such drugs in patients being managed with therapeutic hypothermia (TH) has always been debated. In light of this controversy, this systematic review and meta-analysis was performed to enhance existing literature. Various databases were searched for potentially relevant studies from inception to April 2023, including PubMed, Cochrane Library, and Scopus. The risk of bias was assessed using the Newcastle-Ottawa scale for cohort studies and the Cochrane risk of bias tool for randomized controlled trials. Outcomes of interest included risk of bleeding, stent thrombosis, and all-cause mortality. Five studies were shortlisted for inclusion into the meta-analysis, featuring a total of 245 patients receiving either clopidogrel or ticagrelor. Overall, no significant differences were noted when the use of clopidogrel and ticagrelor was compared in PCI patients being managed with TH. To the best of our knowledge, this is the most comprehensive meta-analysis comparing the outcomes of clopidogrel and ticagrelor in PCI patients being managed with TH. Despite existing studies claiming an altered efficacy of clopidogrel in such conditions, our meta-analytic findings could not prove this relationship. Due to the limited sample size, further comprehensive and randomized studies are encouraged to arrive at a robust conclusion.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.050 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".