Comparison of Antiplatelets and Anticoagulants for Secondary Stroke Prevention in Ischemic Stroke Patients with Cancer: A Meta-Analysis
Bibliographic record
Abstract
This study aims to assess the efficacy and safety of antiplatelets (oral and subcutaneous) and anticoagulants for secondary stroke prevention in ischemic stroke patients with cancer, where the optimal antithrombotic strategy remains unclear. A systematic review and meta-analysis were conducted using PubMed, Cochrane Library, and Scopus (inception to June 2024). Studies were screened based on predefined criteria and assessed with the Newcastle-Ottawa Scale. A random-effects model (R 4.1.2) was used to calculate odds ratios (OR) with 95% confidence intervals (CI). A total of four studies were included in the final analysis. The cumulative sample size was 2,781 participants: 2,204 (79.2%) were treated with antiplatelets and 577 (20.7%) were treated with anti-coagulants. The mean age (± SD) of the patients was 69.56 (±9.88) years, and 65.7% were men. There was no difference in the risk of recurrent ischemic stroke between antiplatelets and anticoagulants (OR = 0.74, 95% CI: 0.31-1.77, 3 studies with 700 patients). There were no differences in the risk of gastrointestinal hemorrhage (OR = 1.74, 95% CI: 0.17-17.46, 2 studies with 2,101 patients) and any major hemorrhage (OR = 0.70, 95% CI: 0.24-2.05, 4 studies with 2781 patients). The odds of all-cause mortality were lower in patients treated with antiplatelets (OR = 0.73, 95% CI: 0.59-0.90, 4 studies involving 2,781 patients). Antiplatelets and anticoagulants showed no difference in recurrent stroke or major hemorrhagic events, but antiplatelets were associated with lower mortality. However, due to limited data, these findings may not fully answer the clinical question, highlighting the need for further high-quality studies.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.065 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".