Parametric survival analysis of menarche onset timing among Nigerian girls
Bibliographic record
Abstract
BACKGROUND: This study was a response to the dearth of information on the timing of menarche in low-income countries, and the need to update knowledge on the condition. It thereby enables the provision of adequate support to young girls during menarche. The study determined the timing and range of onset of menarche and identified the factors influencing the timing. METHODS: We used data on girls' sexual and reproductive processes from a nationally representative population survey of girls aged 15-24 years in Nigeria. Descriptive statistics, and survival analysis techniques were used for data analysis at p = 0·05. FINDING: A quarter of the respondents (26%) had commenced menstruation by age 12. Almost all, (90%) had experienced menstruation by age 17. Girls aged 20-24 years reported later menarche (time ratio 1·066, 95% CI: 1·045-1·087) compared to those aged 15-19 years. An increase of respondents age by one year resulted in 0·8% delay in onset timing. Significant differences were also found in the zone of residence among the sampled population. Compared with girls from the South East, the timing of menstruation was generally delayed among the girls from South-South by 5%, North Central by 9%, South West by 10%, North East by 16% and 17% among girls from the North West. INTERPRETATION: There was a wide range in menarcheal age in Nigerian girls with a peak at 13-14 years and the possibility of a secular trend in the timing of onset. Early family life education is recommended.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".