Sex-based differences in long-term outcomes after stroke: A meta-analysis
Bibliographic record
Abstract
BACKGROUND: There is limited data on sex-related disparities in the long-term outcomes after stroke. We aim to investigate whether there are sex-based differences in long-term outcomes using pooled data. METHODS: Three databases (PubMed, Embase, and Cochrane Library) were systematically searched from inception to July 2022. This meta-analysis was performed in accordance with the recommendations and guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The modified Newcastle-Ottawa scale was used to assess the risk of bias. In addition, a random-effects model was used. RESULTS: Twenty-two cohort studies with 84538 patients were included. There were 50.2% men and 49.8% women. Women had a higher mortality at 1 (odds ration [OR], 0.82; 95% confidence interval [CI][0.69, 0.99], P = 0.03) and 10 (OR 0.72, 95% CI[0.65, 0.79], P < 0.00001) years, higher stroke recurrence at 1 year (OR 0.85, 95% CI[0.73, 0.98], P = 0.02), lower favorable outcome at 1 year (OR 1.36, 95% CI[1.24, 1.49], P < 0.00001). No significant difference was detected between men and women in the outcomes of health-related quality of life and depression. CONCLUSION: In this meta-analysis, the 1- and 10-year mortality and stroke recurrence rates were higher in female patients than in male patients after stroke. In addition, females tended to experience less favorable outcomes in the first year after stroke. Finally, further long-term studies on sex disparities in stroke prevention, care, and management are warranted to explore the opportunities to reduce this gap.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.065 |
| Bibliometrics | 0.006 | 0.005 |
| 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) | 0.003 | 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".