Bibliographic record
Abstract
IntroductionWomen who suffer stroke are older than men, have more cardioembolic aetiology including atrial fibrillation (AF) and suffer from more severe strokes. We aimed to investigate whether the use of antithrombotic drugs is different in women and men with stroke and AF.Methods: We used data from the Norwegian Stroke Registry 2016 and extracted people with detected AF, both known and detected during the hospital stay. Using the Chi-square test we compared the use of different antithrombotic drugs prior to stroke and upon discharge. Results: Out of 8650 patients, 2290 (26.5%) had AF. There were significantly more AF among women than men (27.7 vs 25.5%, p = 0.02). On admission, there were no differences between women and men in the use of aspirin (31.2 vs 32.1%, p = 0.495), clopidorgrel (1.9 vs 2.5%, p = 0.327) or warfarin (21.8 vs 23.6, p = 0.30). Women were less likely to be on treatment with other anticoagulants (19.4 vs 23.4%, p = 0.017). At discharge, there were no differences in the use of either warfarin or other anticoagulants, however, men were more often on treatment with aspirin (17.1 vs 21.8%, <0.001) and clopidogrel (1.8 vs 4.1%, p = 0.002). Conclusion: The proportion of AF is higher among women than among men. Women were less likely than men to be treated with other anticoagulants prior to the stroke, but not on discharge. Men with AF received more antiplatelet drugs on discharge than women with AF, and this could reflect differences in age or other comorbidities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".