Heavy menstrual bleeding in women on anticoagulant treatment for venous thromboembolism: Comparison of high‐ and low‐dose rivaroxaban with aspirin
Bibliographic record
Abstract
BACKGROUND: Rivaroxaban may induce heavy menstrual bleeding. It is unknown if this effect is dose related or if rivaroxaban is associated with more menstrual bleeding than aspirin. OBJECTIVES: To demonstrate and compare menstrual patterns and actions taken among women receiving aspirin and two doses of rivaroxaban. METHODS: The EINSTEIN-CHOICE trial compared once-daily rivaroxaban 20 mg, rivaroxaban 10 mg, and aspirin 100 mg for extended treatment of venous thromboembolism in patients who had completed 6 to 12 months of anticoagulant therapy. In 362 women with menstrual cycles, menstrual flow duration and intensity assessed at days 30, 90, 180, and 360 were compared with those before starting anticoagulant therapy. RESULTS: Menstrual flow duration increased in 12%-18% of the 134 women given 20-mg rivaroxaban, in 6% to 12% of 120 women given 10-mg rivaroxaban, and in 9% to 12% of 108 women given aspirin. Corresponding increases in flow intensity were 19% to 24%, 14% to 21%, and 13% to 20%. The odds ratios (ORs) for increased menstrual flow duration were 1.36 (95% confidence interval [CI], 0.62-2.96) for rivaroxaban 20 mg versus aspirin, 0.77 (95% CI, 0.33-1.81) for rivaroxaban 10 mg versus aspirin, and 0.57 (95% CI, 0.26-1.25) for rivaroxaban 10 mg versus 20 mg. The ORs for increased menstrual flow intensity were 1.41 (95% CI, 0.67-2.99), 1.07 (95% CI, 0.49-2.34), and 0.76 (95% CI, 0.37- 1.57), respectively. CONCLUSIONS: There were no statistically significant differences in menstrual hemorrhage patterns between women treated with 10 or 20 mg of rivaroxaban and aspirin. Compared with 10-mg rivaroxaban or aspirin, 20-mg rivaroxaban showed numerically more often increased menstrual flow duration and intensity.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".