Progesterone therapy increases free thyroxine levels—data from a randomized placebo‐controlled 12‐week hot flush trial
Bibliographic record
Abstract
OBJECTIVE: Thyroid hormones and progesterone both influence core temperature, metabolism and are crucial during pregnancy. Our objective was to discover whether progesterone therapy caused changes in thyroid physiology compared with placebo. DESIGN: Post hoc analysis from a randomized (1:1) placebo-controlled 12-week trial of oral micronized progesterone (Progesterone, 300 mg/d at bedtime) for hot flushes (vasomotor symptoms, VMS) conducted in an academic medical centre. PATIENTS: Postmenopausal euthyroid, healthy (without cardiovascular diseases or risks) women, 1-11 years since last flow on no thyroid or ovarian hormone therapy with VMS participated. MEASUREMENTS: Primary outcomes were final and 12-week changes in TSH, FreeT3 and FreeT4 on progesterone vs placebo. RESULTS: Women with thyroid data (69 of 133 in original trial) were randomized to progesterone (n = 39) or placebo (n = 30)-baseline thyroid values were normal. There were no VMS-thyroid interactions-VMS Score (number × intensity) did not correlate with TSH, FreeT3 or FreeT4 (Spearman's rank correlations: -0.03 to -0.19, respectively; all P > 0.15). At 12 weeks on progesterone, TSH levels tended to be lower (1.7 mU) than on placebo (2.2), P = 0.06; FreeT4 levels were higher (16.4 pmol/l) than on placebo (15.3), P = 0.02. FreeT3 was unchanged throughout. Analysis of covariance showed a significant increase in FreeT4 on progesterone (+2.5 pmol/l; 1.9-3.0) vs on placebo (+1.7; 1.1-2.4) with 95% CI of difference = 0.8 pmol/l [0.0, 1.6], P = 0.04. CONCLUSIONS: Progesterone caused a significant FreeT4 increase that was discovered during this randomized controlled VMS trial. The clinical importance of this increased FreeT4 level remains to be documented. Registered at ClinialTrials.gov#NCT00152438.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".