Do Outcomes between Women and Men Differ after Endovascular Thrombectomy? A Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Research on the presence of sex-based differences in the outcomes of patients undergoing endovascular thrombectomy for acute ischemic stroke has reached differing conclusions. PURPOSE: This review aimed to determine whether sex influences the outcome of patients with large-vessel occlusion stroke undergoing endovascular thrombectomy. STUDY SELECTION: We performed a systematic review and meta-analysis of endovascular thrombectomy studies with either stratified cohort outcomes according to sex (females versus males) or effect size reported for the consequence of sex versus outcomes. We included 33 articles with 7335 patients. DATA ANALYSIS: We pooled ORs for the 90-day mRS score, 90-day mortality, symptomatic intracranial hemorrhage, and recanalization. DATA SYNTHESIS: Pooled 90-day good outcomes (mRS ≤ 2) were better for men than women (OR = 1.29; 95% CI, 1.09–1.53; P = <.001, I2 = 56.95%). The odds of the other outcomes, recanalization (OR = 0.94; 95% CI, 0.77–1.15; P = .38, I2 = 0%), 90-day mortality (OR = 1.11; 95% CI, 0.89–1.38; P = .093, I2 = 0%), and symptomatic intracranial hemorrhage (OR = 1.40; 95% CI, 0.99–1.99; P = .069, I2 = 0%) were comparable between men and women. LIMITATIONS: Moderate heterogeneity was found. Most studies included were retrospective in nature. In addition, the randomized trials included were not specifically designed to compare outcomes between sexes. CONCLUSIONS: Women undergoing endovascular thrombectomy for large-vessel occlusion have inferior 90-day clinical outcomes. Sex-specific outcomes should be investigated further in future trials as well as pathophysiologic 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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.044 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".