Ethnic variation in the onset age of menopause and its consequences on women’s health
Bibliographic record
Abstract
Abstract Background: Reproduction is a major determinant of health in women. To investigate whether female reproductive physiology is still evolving in different ethnic populations, the relationship between ethnicity and onset age of menopause was examined. The relationship between menopause and women’s health was additionally investigated to reflect on the increased risk of serious health conditions following the hormonal fluctuations associated with menopause. Methods: All data used to complete the study’s statistical analyses were obtained from the Study of Women’s Health Across the Nation (SWAN). Women from five ethnicities were included: African American, Chinese, Japanese, Caucasian, and Hispanic. The onset ages of early perimenopause, late perimenopause, and post-menopause were considered, as well as race and diagnoses of stroke, heart attack, osteoporosis, and diabetes. Both ANOVA and Tukey’s honestly significant difference (HSD) tests were performed. Results: We found that the population of Chinese women reached late perimenopause and the population of Japanese women reached both late perimenopause and post-menopause significantly later than African American women. The cohort of Hispanic women reached late perimenopause significantly earlier than most ethnicities, excluding African American women, and reached post-menopause significantly later than all ethnicities. Various associations between the onset of menopause and diagnoses of serious health conditions, including osteoporosis, heart attack, and stroke, were found, individually and in combination with other variables. Conclusions: The ethnicity-related variation found within the onset age of late perimenopause and post-menopause in the SWAN populations suggests that female reproductive changes are still evolving in different ethnicities. The associations found between menopause and women’s health suggest that menopause plays a substantial role in the diagnosis of serious health conditions such as heart attacks, stroke, and osteoporosis. Future studies should consider the impact of both biological (menopause) and non-biological factors on the variation seen in middle-aged women’s health and focus on practical applications in women’s healthcare.
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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) | 0.002 | 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".