Secular trends in age at menarche among women born between 1955 and 1985 in Southeastern China
Bibliographic record
Abstract
BACKGROUND: Improvements in socioeconomic conditions and population health have been linked to declining age at menarche. In China, secular trends in age at menarche following extensive economic reform during recent decades have not been thoroughly investigated. This study examined the overall trend in age at menarche and assessed differences in the rate of change of age at menarche over time, and between urban and rural populations and education levels in southeastern China. METHODS: Age at menarche was retrospectively collected from 1,167,119 Han Chinese women born 1955-1985, who registered in the Perinatal Health Care Surveillance System in 19 cities and counties in two southeast provinces during 1993-2005. Multivariable linear regression was used to estimate trends in age at menarche overall and stratified by urban/rural residence and education level. RESULTS: Age at menarche declined by 0.33 [95% CI 0.33, 0.32] years/decade overall, with the fastest decline in women born in 1966-1975. For the earliest birth cohorts (1955-1965), age at menarche declined faster in urban versus rural regions, and for women with high school education or above versus primary school or less. In contrast, age at menarche declined slower among urban women born 1976-1985, and among those with higher education born 1966-1985. CONCLUSIONS: Mean age at menarche declined for women born in 1955-1985 in southeast China. Further study is warranted to identify specific factors contributing to earlier age at menarche and associated health outcomes.
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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 | 0.000 | 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".