Association between healthy lifestyle pattern and early onset of puberty: based on a longitudinal follow-up study
Bibliographic record
Abstract
The present study aimed to explore the association between healthy lifestyle pattern and childhood early onset of puberty. Based on a cohort study in Xiamen of China, a total of 1294 children was followed for three and a half years. Children's lifestyles, including dietary behaviour, physical activity, sleep duration, smoking and drinking behaviour and sedentary behaviour, were collected by questionnaires. Healthy lifestyle pattern was determined mainly according to the recommendations by the Dietary Guidelines for Chinese school-age children and Canadian Guidelines for children and youth. The pubertal development was assessed by clinical examination according to Tanner stages. The association between pre-pubertal lifestyle and early onset of puberty was estimated using linear regression and log-binomial regression. We found that children who adhered to a healthy lifestyle had a 0·36-year delay of the age of puberty onset (coef = 0·36, 95 % CI (0·08, 0·65)) and 53 % lower risk of early onset of puberty (risk ratio = 0·47, 95 % CI (0·27, 0·80)), compared with those who had a poor lifestyle. However, the beneficial effect of favourable lifestyles on the early onset of puberty was found only in boys with normal weight. Boys who adhered to active physical activity and low sedentary behaviour had a relatively delayed age of puberty onset (coef = 0·49, 95 % CI (0·26, 0·72)). This is the first time to find that healthy lifestyle pattern was associated with a substantially lower risk of early onset of puberty, especially in boys with normal weight. Advocating an integrated healthy lifestyle is essential for the development of children.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".