Impact of lifetime lactation on the risk and duration of frequent vasomotor symptoms: A longitudinal dose–response analysis
Bibliographic record
Abstract
OBJECTIVE: To examine the association between lifetime lactation and risk and duration of frequent vasomotor symptoms (VMS). DESIGN: Prospective cohort. SETTING: USA, 1995-2008. SAMPLE: 2356 parous midlife women in the Study of Women's Health Across the Nation. METHODS: Lifetime lactation was defined as the duration of breastfeeding across all births in months. We used generalised estimating equations to analyse risk of frequent VMS and Cox regression to analyse duration of frequent VMS in years. MAIN OUTCOME MEASURES: Frequent VMS (hot flashes and night sweats) were measured annually for 10 years, defined as occurring ≥6 days in the past 2 weeks. RESULTS: Overall, 57.1% of women reported hot flashes and 43.0% reported night sweats during follow-up. Lifetime lactation was inversely associated with hot flashes plateauing at 12 months of breastfeeding (6 months: adjusted odds ratio [AOR] 0.85, 95% confidence interval (CI) 0.75-0.96; 12 months: AOR 0.78, 95% CI 0.65-0.93) and was inversely associated with night sweats in a downward linear fashion (6 months: AOR 0.93, 95% CI 0.81-1.08; 18 months: AOR 0.82, 95% CI 0.67-1.02; 30 months: AOR 0.73, 95% CI 0.56-0.97). Lifetime lactation was associated with shorter duration of hot flashes and night sweats in a quadratic (bell-shaped) fashion. The association was strongest at 12-18 months of breastfeeding and significant for hot flashes (6 months: adjusted hazard ratio [AHR] 1.35, 95% CI 1.11-1.65; 18 months: AHR 1.54, 95% CI 1.16-2.03; 30 months: AHR 1.18, 95% CI 0.83-1.68). CONCLUSIONS: Longer lifetime lactation is associated with decreased risk and duration of frequent VMS.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".