Endovascular treatment decision in acute stroke: does physician gender matter? Insights from UNMASK EVT, an international, multidisciplinary survey
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Differences in the treatment practice of female and male physicians have been shown in several medical subspecialties. It is currently not known whether this also applies to endovascular stroke treatment. The purpose of this study was to explore whether there are differences in endovascular treatment decisions made by female and male stroke physicians and neurointerventionalists. METHODS: In an international survey, stroke physicians and neurointerventionalists were randomly assigned 10 case scenarios and asked how they would treat the patient: (A) assuming there were no external constraints and (B) given their local working conditions. Descriptive statistics were used to describe baseline demographics, and the adjusted OR for physician gender as a predictor of endovascular treatment decision was calculated using logistic regression. RESULTS: 607 physicians (97 women, 508 men, 2 who did not wish to declare) participated in this survey. Physician gender was neither a significant predictor for endovascular treatment decision under assumed ideal conditions (endovascular therapy was favored by 77.0% of female and 79.3% of male physicians, adjusted OR 1.03, P=0.806) nor under current local resources (endovascular therapy was favored by 69.1% of female and 76.9% of male physicians, adjusted OR 1.03, P=0.814). CONCLUSION: Endovascular therapy decision making between male and female physicians did not differ under assumed ideal conditions or under current local resources.
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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 | 0.014 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".