Sex Differences and Similarities in the Management and Outcome of Stroke Patients
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Previous studies have documented sex differences in the management and outcome of patients with cardiovascular disease. However, little data exist on whether similar sex differences exist in stroke patients. We conducted a study to determine whether sex differences exist in patients with acute stroke admitted to Ontario hospitals. METHODS: Using linked administrative databases, we performed a population-based cohort study. The databases contained information on all 44 832 patients discharged from acute-care hospitals in Ontario between April 1993 and March 1996 with a most responsible diagnosis of acute stroke. The main outcomes measured consisted of sex differences in comorbidities, the use of rehabilitative services, the use of antiplatelet therapy and anticoagulants (in elderly stroke survivors aged > or =65 years only), discharge destination, and mortality. RESULTS: Male stroke patients were more likely than female stroke patients to have a history of ischemic heart disease (18.1% versus 15.3%, respectively; P<0.001) and diabetes mellitus (20.1% versus 18. 7%, respectively; P<0.001), whereas female patients were more likely than male patients to have hypertension (33.8% versus 30.0%, respectively; P<0.001) and atrial fibrillation (12.9% versus 10.2%, respectively; P<0.001). There were no sex differences in the usage of in-hospital rehabilitative services. The overall 90-day postdischarge use of aspirin and ticlopidine was similar in stroke survivors aged 65 to 84 years. However, among stroke survivors aged > or =85 years, men were more likely than women to receive aspirin (36. 0% versus 30.7%, respectively; P<0.001) and ticlopidine (9.2% versus 6.8%, respectively; P=0.007). Use of warfarin was similar for the two sexes. Men were more likely than women to be discharged home (50. 6% versus 40.9%, respectively; P<0.001) and less likely to be discharged to chronic care facilities (16.8% versus 25.2%, respectively; P<0.001). The risk of death 1 year after stroke was somewhat lower in women than men (adjusted odds ratio 0.939, 95% CI 0.899 to 0.980; P=0.004). The mortality differences were greatest among elderly stroke patients. CONCLUSIONS: Elderly men are more likely than elderly women to receive aspirin and ticlopidine and equally like to receive warfarin after a stroke. Despite these differences, elderly women have a better 1-year survival after a stroke.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".