E-110 Endovascular treatment decision in acute stroke: does physician sex matter? insights from an international multidisciplinary survey
Bibliographic record
Abstract
Introduction Numerous studies have compared treatment approaches of female and male physicians in different medical subspecialties, some of them revealed significant differences. To date, only few women are engaged in the neurointerventional field. Hence, it is unclear whether the treatment practice is influenced by physicians’ sex. We explored whether there are differences in treatment decisions made by female and male physicians. Materials and methods An international cross-sectional survey of both female and male stroke physicians and neurointerventionalists was conducted. Participants were randomly assigned 10 cases out of a pool of 22 case scenarios and asked how they would treat the patient A) assumed there were no economical or infrastructural constraints, and B) given their current working conditions. Subgroup analyses were performed for female and male physicians respectively. Results 607 physicians (97 women, 508 men, 2 who did not disclose their sex), of different specialties (326 neurologists, 173 interventional neuroradiologists, 81 interventional neurosurgeons, 2 geriatricians, 5 internists, 20 other) from 38 countries participated in this survey. 6070 responses were obtained. Neurologists constituted the largest group of both female (76.3%) and male (49.2%) physicians, with a more even distribution of specialties in male physicians. Assuming ideal conditions, no significant differences in EVT decision making was observed (EVT was favored by 77% of female and 79.3% of male physicians). Under their current working conditions, female physicians decided less frequently in favor of EVT (69.1%) as compared to their male colleagues (76.9%, p<0.001). Conclusion Under the ideal conditions, EVT decision between male and female physicians did not differ. Current working conditions restricted female physicians’ endovascular treatment decision to a greater degree as compared to their male colleagues, resulting in a significantly lower decision rate in favor of EVT. Disclosures J. Ospel: None. N. Kashani: None. B. Campbell: None. M. Foss: None. F. Turjman: None. S. Yoshimura: None. A. Wilson: None. W. Kunz: None. M. Cherian: None. B. Kim: None. A. Rabinstein: None. U. Fischer: None. P. Sylaja: None. B. Baxter: None. J. Heo: None. B. Menon:None. G. Saposnik: None. M. Hill: None. M. Goyal: None. M. Almekhlafi: 1; C; unrestricted research grant by Stryker to University of Calgary.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 0.001 |
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".