Effects of E4/DRSP on self-reported physical and emotional premenstrual and menstrual symptoms: data from the phase 3 clinical trial in Europe and Russia
Bibliographic record
Abstract
Purpose To describe the effects of estetrol (E4) 15 mg/drospirenone (DRSP) 3 mg on physical and emotional premenstrual and menstrual symptoms.Materials and Methods We used Menstrual Distress Questionnaire (MDQ) data from a phase-3 trial (NCT02817828) in Europe and Russia with participants (18 − 50 years) using E4/DRSP for up to 13 cycles. We assessed mean changes in MDQ-t-scores from baseline to end of treatment in premenstrual (4 days before most recent flow) and menstrual (most recent flow) scores for 4 MDQ domains in starters and switchers (use of hormonal contraception in prior 3 months) and performed a shift analysis on individual symptoms within each domain.Results Of 1,553 treated participants, 1,398(90.0%), including 531(38%) starters, completed both MDQs. Starters reported improvements for premenstrual Pain (−1.4), Water Retention (−3.3) and Negative Affect (−2.5); and for menstrual Pain (−3.5), Water Retention (−3.4), and Negative Affect (−2.7) (all p < 0.01). For switchers, no changes were significant except an increase in premenstrual (+1.0, p = 0.02) and menstrual (+1.5, p = 0.003) Water Retention. We observed a change in symptom intensity in >40% of participants for Cramps, Backache and Fatigue (domain Pain), Painful or Tender Breast and Swelling (domain Water Retention) and Mood Swings and Irritability (domain Negative Affect).Conclusion E4/DRSP starters experienced significant improvements in the domains Pain, Water Retention and Negative Affect particularly benefiting those with more severe baseline symptoms. Switchers showed minimal changes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".