Abstract 083: Comparison of Antiplatelets and Anticoagulants for Secondary Stroke Prevention in Ischemic Stroke Patients with Cancer: A Systematic Review and Meta Analysis
Bibliographic record
Abstract
Background Ischemic stroke patients with underlying cancer remain at high risk for subsequent stroke but the appropriate choice for secondary stroke prevention is not known. Objective To compare the efficacy and safety of antiplatelets and anticoagulants for secondary stroke prevention ischemic stroke patients with underlying cancer. Methods We conducted a systematic review of the literature using MEDLINE (via PubMed), the Cochrane Library, and Scopus, and included all relevant studies published from inception to June 2024. Articles were screened according to predefined eligibility criteria, and relevant studies were assessed for quality using the validated Newcastle‐Ottawa Scale. The odds ratio (OR) with 95% confidence intervals (CI) were calculated using random effect model (meta package in R (version 4.1.2, R Foundation for Statistical Computing). Results A total of 8 studies were included in the final analysis with cumulative sample size of 4,588 participants: 2,203 (48%) in the antiplatelet group and 2,128 (46%) in the anticoagulant group. The mean age of the patients was 69.44 ± 10.02 years, with 59.7% were male and 93.6% had solid tumors. A pool analysis of three studies showed a 13 % of overall complication rate for anti‐platelet therapy (95% CI: 5‐35%, P<0.01). Anti‐coagulant therapy had a 8% of overall complication rate across three studies (95% CI: 2‐22%, P<0.01). No significant difference in overall complication odds was found between the therapies (OR 0.83, 95% CI 0.34‐2.01, p = 0.26). Anti‐platelet therapy showed a trend towards lower recurrent stroke odds (OR = 0.6441, p = 0.2203). Gastrointestinal bleeding odds were higher with anti‐platelets (OR = 1.7425, p = 0.6367), and ICH odds were lower (OR = 0.7238, p = 0.2367). Major bleeding odds showed no significant difference (OR = 0.66, 95% CI 0.36‐1.22, p = 0.35), but anti‐platelets significantly reduced death odds (OR = 0.7203, 95% CI 0.54‐0.95, p = 0.022). Conclusion There were no differences in the rates of recurrent ischemic stroke or hemorrhagic events in ischemic stroke patients with underlying cancer treated with antiplatelets or anticoagulants. There was lower mortality observed in patients treated with antiplatelets.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.049 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".