Women and stroke: disparities in clinical presentation, severity, and short- and long-term outcomes
Bibliographic record
Abstract
Objectives There are limited data from the Middle East on sex-related differences in short- and long-term stroke outcomes. We present 8 years of experience based on the Qatar stroke database. Setting The Qatar stroke database prospectively collects data on all stroke patients admitted to Hamad General Hospital. For this study, we compared female and male acute ischemic stroke patients on their characteristics at admission, short-term outcomes [modified Rankin Scale (mRS) score], and long-term outcomes [incidence of major adverse cardiovascular events (MACEs)]. Participants A total of 7,300 patients [F: 1,406 (19.3%), M: 5,894 (80.7%); mean age 55.1 ± 13.3 (F: 61.6 ± 15.1, M: 53.5 ± 12.3; p < 0.001)] were admitted with acute ischemic stroke. Results Significantly fewer women presented within 4.5 h of onset (F: 29% vs. M: 32.8%; p = 0.01). Although women were more likely to experience severe stroke (NIHSS >10; F: 19.9% vs. M: 14.5%; p < 0.001), fewer were treated with thrombolysis (F: 9.8% vs. M: 12.1%; p = 0.02). Women experienced more medical complications (F: 11.7% vs. M: 7.4%; p < 0.001) and tended to have a more prolonged length of stay in the hospital (F: 6.4 ± 7.6 days vs. M: 5.5 ± 6.8 days; p < 0.001). Primary and secondary outcome measures Good outcomes at 90 days (mRS score of 0–2) were less frequent in women (F: 53.3% vs. M: 71.2%; p < 0.001). Fewer female patients were taking antiplatelets (F: 78% vs. M: 84.8%; p < 0.001) or statins (F: 81.2% vs. M: 85.7%; p < 0.001). Significantly more female patients experienced a MACE (F: 12.6% vs. M: 6.5%; p < 0.001). Conclusion Older age at presentation contributes to poor outcomes following acute stroke in women. Other contributing factors include delays in admission to the hospital, lower rates of thrombolysis, and lower rates of provision of preventative treatments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".