Hot flushes and night sweats differ in associations with cardiovascular markers in healthy early postmenopausal women
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the associations between vasomotor symptoms ([VMS] hot flushes or flashes and night sweats) and markers of cardiovascular risk. METHODS: Healthy postmenopausal women in a randomized controlled trial of progesterone for VMS recorded VMS frequency in the Daily Menopause Diary for 28 days at baseline. Accepted risks for cardiovascular disease were measured: body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), blood pressure (BP), endothelial function by venous occlusion plethysmography, fasting lipids, glucose, high-sensitivity C-reactive protein, albumin, and D-dimer. Relationships between risk variables and VMS frequency (24 h, day and night) were assessed by univariate and multivariate robust regressions with adjustment for age and WHtR. RESULTS: Data were available for 145 healthy, nonsmoking women without heart disease, hypertension, or diabetes who were 1 to 11 years past their final menstruation and were aged 43 to 65 years, with a mean (SD) BMI of 25.0 (2.9) kg/m and WC of 79.1 (7.1) cm. Anthropometric variables (BMI, WC, and WHtR) were significantly negatively associated with total (24-h day) VMS frequency and with day VMS but not with night VMS frequency. Systolic BP decreased with greater 24-hour VMS frequency, and both systolic and diastolic BPs were inversely related to day but not night VMS frequency. Albumin was positively associated with night VMS frequency but not with day or 24-hour VMS frequency. Other variables showed little association with VMS frequency. CONCLUSIONS: Hot flushes, but not night sweats, were associated with lower cardiovascular risk factors in these healthy postmenopausal women. Future research should differentiate night sweats from hot flushes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".