Oral micronized progesterone for perimenopausal night sweats and hot flushes a Phase III Canada-wide randomized placebo-controlled 4 month trial
Bibliographic record
Abstract
This study tested progesterone for perimenopausal hot flush ± night sweat (vasomotor symptom, VMS) treatment. It was a double-blind, randomized trial of 300 mg oral micronized progesterone@bedtime versus placebo for 3-months (m) after a 1-m untreated baseline during 2012/1-2017/4. We randomized untreated, non-depressed, screen- and baseline-eligible by VMS, perimenopausal women (with flow within 1-year), ages 35-58 (n = 189). Participants aged 50 (± SD = 4.6) were mostly White, educated, minimally overweight with 63% in late perimenopause; 93% participated remotely. The 1° outcome was 3rd-m VMS Score difference. Participants recorded VMS number and intensity (0-4 scale)/24 h on a VMS Calendar. Randomization required VMS (intensity 2-4/4) of sufficient frequency and/or ≥ 2/week night sweat awakenings. Baseline total VMS Score (SD) was 12.2 (11.3) without assignment difference. Third-m VMS Score did not differ by therapy (Rate Difference - 1.51). However, the 95% CI [- 3.97, 0.95] P = 0.222, did not exclude 3, a minimal clinically important difference. Women perceived progesterone caused decreased night sweats (P = 0.023) and improved sleep quality (P = 0.005); it decreased perimenopause-related life interference (P = 0.017) without increased depression. No serious adverse events occurred. Perimenopausal night sweats ± hot flushes are variable; this RCT was underpowered but could not exclude a minimal clinically important VMS benefit. Perceived night sweats and sleep quality significantly improved.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".