No disadvantages for women in acute stroke care in Germany: an analysis of access to stroke treatment services in Germany from 2017 to 2022
Bibliographic record
Abstract
BACKGROUND: Several publications have raised concerns that female stroke patients may be at a disadvantage when accessing stroke treatment services. These publications have found significant regional differences in the provision of stroke treatment to male and female patients. In this study, we provide current nationwide data on stroke management differences between men and women in Germany. METHODS: This large retrospective cohort study used national datasets from the German Federal Statistical Office for 2017-2022. We examined differences between female and male stroke patients in terms of case volume, intravenous thrombolysis (IVT), mechanical thrombectomy (MTE), stroke unit (SU) treatment, intrahospital mortality, and prevalence of atrial fibrillation (AF). RESULTS: Data from more than 1.3 million hospitalised stroke patients were included. Forty-seven percent of the patients were female. Female patients were older and more frequently ≥ 80 years old (50.3% versus 29.4%). Rates of IVT (16.3% versus 16.3%) were similar for both sexes but higher in females when adjusted for age. MTE rates (8.2% versus 6.3%) were consistently higher in female patients across all age groups. Female patients had higher rates of intrahospital mortality (9.1% versus 6.2%), and admission to SUs (73.6% versus 76.0%) was less common. Treatment rates in intensive care units were similar (10.6% versus 10.5%). AF, a surrogate for embolic (and more severe) strokes, was more prevalent in females (32.6% versus 25.4%). CONCLUSIONS: We found no evidence that female stroke patients in Germany face any disadvantage in accessing stroke treatment services. Acute stroke treatment rates were generally similar or higher when compared to males. Higher intrahospital mortality and lower SU rates were attributed to greater age, comorbidities, and stroke severity. However, the differences were not fully explained when adjusting for AF and age. Further research is needed on sex differences in stroke mechanisms and outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".