Vasomotor symptoms and cardiometabolic risk factors in menopausal women: a MONET Group study
Bibliographic record
Abstract
INTRODUCTION: Conflicting results have been reported concerning the prevalence of cardiometabolic risk factors in women experiencing vasomotor symptoms (VMS). OBJECTIVES: To compare cardiometabolic risk factors between women with and without VMS during the menopause transition and to determine the influence of physical activity on the prevalence of VMS. METHODS: Yearly assessment of women transitioning through menopause included self-reported VMS (hot flushes and night sweats), body composition and fat distribution, fasting glucose, insulin and lipids, and physical activity levels. RESULTS: Eighty-five of the 102 premenopausal women at baseline were included (age: 49.9 ± 2.0 years; body mass index: 23.2 ± 2.2 kg/m(2)). According to linear mixed model analyses, no statistically significant differences were observed for fat mass, lean body mass, body fat distribution indices and cardiometabolic risk factors, when comparing symptomatic vs. asymptomatic women. Neither physical activity levels nor intensity were associated with the prevalence of VMS. CONCLUSION: Our results suggest that women transitioning through menopause who reported VMS did not show greater deteriorations in body composition, body fat distribution and cardiometabolic risk factors. Furthermore, physical activity levels were not associated with lower prevalence of vasomotor symptoms in the present cohort.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".