Sex-Based Differences in the Effect of Intra-Arterial Treatment of Stroke
Bibliographic record
Abstract
Background and Purpose— Sex influences outcome after intravenous thrombolysis. In a combined analysis of the tissue plasminogen activator clinical trials, a sex-by-treatment interaction was observed. We sought to confirm that observation in an independent data set. Methods— Data were from the Pro-Urokinase for Acute Cerebral Thromboembolism-2 (PROACT-2) trial. Baseline factors were compared by sex. The primary outcome was an assessment of a sex-by-treatment interaction term within a logistic regression model, using a modified Rankin Scale score ≤2 at 90 days as the binary outcome. We also assessed whether there were differences in CT-scan appearance and recanalization at 2 hours post-treatment. Results— In the PROACT-2 study of intra-arterial stroke thrombolysis, in both women and men, prourokinase resulted in better outcomes than control. A sex by prourokinase treatment interaction was observed, with women showing a larger treatment effect (20% absolute benefit) compared with men (10% absolute benefit). The reason for this interaction is that thrombolytic treatment nullifies the worse outcome for untreated women compared with men. The reasons for effect modification do not include improved recanalization at 2 hours among women. Conclusions— Women with middle cerebral artery ischemic stroke benefit more from intra-arterial therapy. Further study of how sex affects stroke outcome is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".