Sex Differences in Dabigatran Use, Safety, And Effectiveness In a Population-Based Cohort of Patients With Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Sex differences were observed with regard to warfarin treatment in patients with atrial fibrillation, with women having a higher risk of stroke compared with men. We aimed to compare sex differences in use, safety, and effectiveness of dabigatran. METHODS AND RESULTS: We conducted a population-based cohort study of patients with atrial fibrillation using administrative data in Quebec, Canada, 1999 to 2013. Men and women who filled a prescription for dabigatran (110 and 150 mg bid) were compared with matched warfarin users with respect to their rates of stroke, bleeding, and myocardial infarction events, using propensity score analysis. The cohort comprised 31 786 women (50.4%) and 31 324 men (49.6%). Women had a higher baseline stroke risk and lower baseline bleeding risk compared with men. Women filled more prescriptions for the lower dabigatran dose compared with men (adjusted OR, 1.35; 95% confidence interval, 1.24-1.48). In multivariable analyses adjusted for propensity scores, dabigatran use was associated with a lower risk of bleeding compared with warfarin in men (P for interaction=0.008). Dabigatran was associated with a trend toward lower risk of stroke in women treated with the 150-mg dose (HR, 0.79; 95% confidence interval, 0.56-1.04), but was not associated with a difference in the risk of myocardial infarction compared with warfarin in either sex. CONCLUSIONS: In real-life practice, women are more frequently treated with low-dose dabigatran, yet a trend toward lower stroke rates in women taking high-dose dabigatran was observed. Men benefit from lower bleeding rates with dabigatran compared with warfarin.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".