Menopausal symptoms within a Hispanic cohort: SWAN, the Study of Women's Health Across the Nation
Bibliographic record
Abstract
INTRODUCTION: Since the designation of people as Hispanic involves the amalgamation of a number of different cultures and languages, we sought to test the hypothesis that menopausal symptoms would differ among Hispanic women, based upon country of origin and degree of acculturation. METHODS: A total of 419 women, aged 42-52 years at baseline, were categorized as: Central American (CA, n = 29) or South American (SA, n = 106), Puerto Rican (PR, n = 56), Dominican (D, n = 42), Cuban (Cu, n = 44) and non-Hispanic Caucasian (n = 142). We assessed vasomotor symptoms, vaginal dryness and trouble in sleeping. Hispanics and non-Hispanic Caucasians were compared using the chi(2) test, t test or non-parametric alternatives; ANOVA or Kruskal-Wallis testing examined differences among the five Hispanic sub-groups. Multivariable regression models used PR women as the reference group. RESULTS: Hispanic women were overall less educated, less acculturated (p < 0.001 for both) than non-Hispanic Caucasians and more of them reported vasomotor symptoms (34.1-72.4% vs. 38.3% among non-Hispanic Caucasians; p = 0.0293) and vaginal dryness (17.9-58.6% vs. 21.1% among non-Hispanic Caucasians, p = 0.0287). Among Hispanics, more CA women reported vasomotor symptoms than D, Cu, SA, or PR women (72.4% vs. 45.2%, 34.1%, 50.9%, and 51.8%, respectively). More CA (58.6%) and D women (38.1%) reported vaginal dryness than PR (17.9%), Cu (25.0%) and SA (31.4%) women. More PR and D women reported trouble in sleeping (66.1 and 64.3%, respectively) compared to CA (51.7%), Cu (36.4%), and SA (45.3%) women. CONCLUSION: Symptoms associated with menopause among Hispanic women differed by country of origin but not acculturation. Central American women appear to be at greatest risk for both vasomotor symptoms and vaginal dryness.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".