Impact of Estradiol Valerate/Dienogest on Work Productivity and Activities of Daily Living in Women with Heavy Menstrual Bleeding
Bibliographic record
Abstract
OBJECTIVES: To quantify the change in work productivity and activities of daily living in North American women with heavy menstrual bleeding (HMB) treated with estradiol valerate/dienogest (E2V/DNG; Qlaira(®)/Natazia(®)) compared to placebo. METHODS: Women in the United States and Canada, aged 20-53 years with an objective diagnosis of HMB and no recognizable anatomical pathology, were treated with E2V/DNG or placebo for seven cycles (196 days). Main outcome measures included work productivity (i.e., productivity while at work) and activities of daily living measured using a modified Work Productivity and Activity Impairment Questionnaire (mWPAI) on a Likert scale from 0 to 10 (higher values denote higher impairment levels). RESULTS: In both countries, significant improvement was observed between baseline and end of treatment in work productivity and activities of daily living impairment. The improvements in work productivity and activities of daily living with E2V/DNG treatment relative to placebo ranged from 37.2% to 39.2% across both countries. Monthly gains due to E2V/DNG treatment (net of placebo improvement) associated with improvement in work productivity were estimated to be US$80.2 and Can$70.8 (US$58.5) and those associated with improvement in activities of daily living were estimated to be US$84.9 and Can$73.5 (US$60.7). CONCLUSIONS: E2V/DNG was shown to have a consistent positive impact on work productivity and activities of daily living in U.S. and Canadian women with HMB. In addition, these improvements in work productivity and activities of daily living were associated with a reduction in HMB-related monetary burden compared to the placebo group.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".