Progesterone for hot flush and night sweat treatment – effectiveness for severe vasomotor symptoms and lack of withdrawal rebound
Bibliographic record
Abstract
A controlled trial recently showed that oral micronized progesterone (Progesterone, 300 mg at h.s. daily) was effective for vasomotor symptoms (VMS) in 133 healthy early postmenopausal women. Here, we present subgroup data in women with severe VMS (50 VMS of moderate-severe intensity/wk) and also 1-mo withdrawal study outcomes. Women with severe VMS (n = 46) resembled the full cohort but experienced 10 VMS/d of 3 of 4 intensity. On therapy, the progesterone VMS number (#) decreased significantly more than placebo # to 5.5/day (d) versus 8/d (ANCOVA -2.0 95% CI: -3.5 to -0.4). Just after trial mid-point, a withdrawal substudy (D/C) was added--56 women were invited and 34 (61%) took part (progesterone 17; placebo 17). Those in the D/C cohort resembled the whole cohort. On stopping, VMS gradually increased--at D/C week 4, on progesterone, VMS daily # reached 78% and significantly less than baseline (-3.0 to -0.8) but placebo VMS # did not differ from run-in. In summary, progesterone is effective for severe VMS and does not cause a rebound increase in VMS when stopped. That progesterone may be used alone for severe VMS and unlike estrogen does not appear to cause a withdrawal rebound increases VMS treatment options.
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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) | 0.001 | 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) | 0.002 | 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".