Sex, Socioeconomic and Regional Disparities in Age Trajectories of Childhood BMI, Underweight and Overweight in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using a longitudinal dataset from the China Health and Nutrition Survey (CHNS), growth curve models were employed to examine age trajectories of BMI for 1,694 subjects who were aged 2-11 in 1993 and followed in four waves (1997, 2000, 2004 and 2006). Based on age- and sex-specific BMI cut-points recommended for international use, the prevalence rates of overweight and underweight in the transition from childhood to adulthood (age 6-18) were also predicted. Sex, family income, rural-urban residency and geographical location were found to be significantly associated with the onsets, slopes, and acceleration of age trajectories in BMI, overweight, and underweight (P<0.01). Children who had lower prevalence of underweight in the transition from childhood to adulthood exhibited higher prevalence of overweight than their counterparts did. Moreover, the age interval during which children were more vulnerable to an increase in underweight was different from that for overweight. There were substantial regional disparities in the age trajectories of childhood overweight and underweight. Whereas the analyses suggest that the dual burden of nutritional problems (the coexistence of overweight and underweight) in China is more like two sides of a coin than two separate health issues, the critical age period for intervening in childhood overweight is different from that of childhood underweight. Geographical indicators of childhood obesity in China deserve further attention.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it