1 and 2 mg 17β-estradiol combined with sequential dydrogesterone have similar effects on the serum lipid profile of postmenopausal women
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to assess the effects of 1 and 2 mg 17beta-estradiol on serum lipid profile. Beneficial effects have been clearly established in previous studies with a 2 mg dose; further evidence was required to confirm the beneficial effects of a 1 mg dose. METHODS: This double-blind, placebo-controlled study involved 579 postmenopausal women randomized to oral treatment with placebo, 1 mg/day 17beta-estradiol sequentially combined with 5 or 10 mg/day dydrogesterone for the last 14 days of each 28-day cycle, or 2 mg/day 17beta-estradiol sequentially combined with 10 or 20 mg/day dydrogesterone for the last 14 days of each 28-day cycle. Treatment was continued for 26 cycles. RESULTS: High density lipoprotein (HDL) cholesterol levels were significantly (p<0.05) increased after 26 cycles in all active treatment groups compared with placebo. In addition, low density lipoprotein (LDL) cholesterol and lipoprotein(a) levels were significantly reduced, and apolipoprotein A1 and triglyceride levels were significantly increased, in all active treatment groups after 13 and 26 cycles. CONCLUSIONS: The results of this study clearly indicate that sequential combinations of either 1 or 2 mg 17beta-estradiol with dydrogesterone are associated with long-term, favorable changes in the serum lipid profile. There was no evidence that dydrogesterone compromised the 17beta-estradiol-induced improvements in lipid profile.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".