Effect of Mifepristone for Symptomatic Leiomyomata on Quality of Life and Uterine Size
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of low-dose mifepristone on quality of life, pain, bleeding, and uterine size among women with symptomatic leiomyomata. METHODS: Forty-two women with symptomatic uterine leiomyomata and uterine volume of 160 mL or more were randomized to mifepristone, 5 mg daily, or placebo for 26 weeks. Quality of life (Uterine Fibroid Symptoms Quality of Life Questionnaire and Medical Outcomes Study 36-Item Short Form survey) and uterine and leiomyoma size (ultrasonography) were assessed at baseline, and at 1 month, 3 months, and 6 months of treatment. Bleeding (daily logs and pictorial charts) and pain (McGill Pain Questionnaire) were assessed monthly. Endometrial pathology was assessed at baseline and 6 months. RESULTS: Forty-two women were randomized; 37 women completed all 6 months. Women randomized to mifepristone showed an improvement in leiomyoma-specific quality of life. Forty-one percent became amenorrheic, rates of anemia improved, and adjusted uterine size was reduced by 47%. Compared with the placebo group, improvements in these outcomes in the treatment group were significantly greater (P<.05 to .001). There were no significant differences in adverse effects between the groups. No endometrial hyperplasia was noted in any participant. CONCLUSION: Low-dose mifepristone improves leiomyoma-specific quality of life and reduces leiomyoma size among women with symptomatic leiomyomata. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov www.clinicaltrials.gov NCT00133705 LEVEL OF EVIDENCE: I.
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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) | 0.001 | 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) | 0.002 | 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".