Gender Differences in Stroke Examined in a 10-Year Cohort of Patients Admitted to a Canadian Teaching Hospital
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Studies suggest that women with stroke are investigated less aggressively and receive tissue plasminogen activator less frequently than men. We tested whether gender differences in the investigation, treatment, and outcome of stroke are due to confounding factors. METHODS: Gender differences in the use of investigations, trial enrollment, treatment with intravenous tissue plasminogen activator, and in-hospital outcomes were examined in data from our prospective registry using multivariate analysis to adjust for age, prestroke functional status, stroke subtype and severity, and atrial fibrillation. RESULTS: Of 2725 consecutive hospitalized patients (1996 to 2006), 88% had ischemic stroke and 48% were women. Women were older (median age, 77 versus 70 years), had more severe strokes, and were less likely to be independent prestroke (78% versus 87%) compared with men (all P<0.001). The proportion of women, but not men, aged >or=80 years, increased significantly between 1996 to 1997 and 2005 to 2006. After adjustment for confounding, women were less likely to have infratentorial strokes (OR, 0.78; 95% CI, 0.62 to 0.97), be able to walk unaided on admission (OR, 0.69; 95% CI, 0.54 to 0.87), be treated with tissue plasminogen activator (OR, 0.51; 95% CI, 0.35 to 0.72), experience pneumonia (OR, 0.38; 95% CI, 0.26 to 0.55), achieve a discharge Barthel Index of >or=95 (OR, 0.75; 95% CI, 0.61 to 0.94, and were more likely to experience a urinary tract infection (OR, 2.06; 95% CI, 1.61 to 2.64). There was no gender difference in adjusted use of investigations. CONCLUSIONS: The majority of the gender differences in stroke were explained by confounding. More research is required to understand gender differences in stroke pathophysiology and the utilization of thrombolytic therapy.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".