Sex Differences in Endovascular Therapy for Ischemic Stroke: Results From the Get With The Guidelines–Stroke Registry
Bibliographic record
Abstract
BACKGROUND: In 2015, endovascular therapy (EVT) for large vessel occlusions became standard of care for acute ischemic stroke. Lower utilization of IV alteplase has been reported in women, but whether sex differences in EVT use in the United States exists has not been established. METHODS: We identified all acute ischemic stroke discharges from Get With The Guidelines-Stroke hospitals between 2012 and 2019 who were potentially eligible for EVT, based on National Institutes of Health Stroke Scale score ≥6 and arrival <6 hours, according to 2018 American Heart Association/ASA guidelines. Multivariable regression analyses were used to determine the association between sex and EVT utilization, and outcomes (including mortality, discharge home, functional status) after EVT. Separate analyses were conducted for the 2 time periods: 2012 to 2014, and 2015 to 2019. RESULTS: Of 302 965 patients potentially eligible for EVT, 42 422 (14%) received EVT. Before 2015, EVT treatment rates were 5.3% in women and 6.6% in men. From 2015 to 2019, treatment rates increased in both sexes to 16.7% in women and 18.5% in men. The adjusted odds ratio for EVT in women compared with men was 0.93 (95% CI, 0.87-0.99) before 2015, and 0.98 (95% CI, 0.96-1.01) after 2015. There were no significant sex differences in outcomes except that after 2015, women were less able to ambulate at discharge (adjusted odds ratio, 0.95 [95% CI, 0.95-0.99]) and had lower in-hospital mortality (adjusted odds ratio, 0.93 [95% CI, 0.88-0.99]). CONCLUSIONS: EVT utilization has increased dramatically in both women and men since EVT approval in 2015. Following statistical adjustment, women were less likely to receive EVT initially, but after 2015, women were as likely as men to receive EVT. After EVT, women were more likely to be disabled at discharge but less likely to experience in-hospital death compared with men.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".