Abstract TP301: Gender Differences in Short- and Long-Term Outcome of Patients With Suspected Acute Stroke
Bibliographic record
Abstract
Background: There are gender differences in the short-term prognosis following acute stroke suggesting that outcome is less favorable in women. Factors contributing to this poor outcome include preexisting morbidity, stroke severity and higher age. Most previous studies have looked at short-term prognosis. PURPOSE: We investigated whether gender differences have a differential impact on incidence of short-term outcome and long-term major adverse cardiovascular events (MACE) including stroke, myocardial infarction, unstable angina, coronary revascularization procedure, and death in patients with suspected acute stroke. Methods: The study used a prospective cohort of Qatari patients with suspected acute stroke between January 2014 and February 2019. We calculated the modified Rankin score (mRS) at discharge and 90-days (short-term) and MACE (long-term) outcomes in both genders. To determine the independent predictor for MACE, the Cox proportional hazards regression analysis was used and summarized as hazard ratio and 95% confidential interval. Results: A total of 1372 patients identified. At 90-days, women found to have significantly poorer outcome (34.0% vs 23.4%, p<0.001) mortality (8.5% vs 5.2%, p<0.03) overall. MACE was present in 30.5% (418/1372) during follow-up (57.2% males and 54.3% females, p=0.32). Median follow-up was 44.6 months for females and 47.2 months for males. Mean age in MACE group was significantly higher (65.5±15.3 vs 60.1±15.9, p< 0.001). Hypertension, diabetes, prior history of stroke, coronary artery disease, and atrial fibrillation on admission was more significant in MACE group, while obesity (BMI ≥ 30 kg/m2) was more common in non-MACE group. Patients with MACE had higher NIHSS on admission (6.1±7.4 vs 3.5±5.3, p<0.001), HbA1c (7.7±2.3 vs 7.4±2.3, p=0.02) and poorer prognosis (44.5% vs 18.6%, p<0.001) and higher mortality at 90-days. Once corrected, the hazard regression analysis showed that no difference in MACE between the two genders. Conclusion: Our results show that despite higher mortality and poor outcome at 90-days, the long-term outcome in women did not show any significant difference from men in this cohort. This may be related to older age and presence of cardiovascular risk factors.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".