Understanding body mass index classification of young children: The interaction with height in more and less stunted subpopulations in a common region: Experience from North West Syria
Bibliographic record
Abstract
Objective: To examine the differential classification of children, using the International Obesity Task Force (IOTF) and the CDC BMI reference standards for nutritional classification and assess any interaction of height‐for‐age Z‐scores (HAZ) with BMI. Methods: We measured Ht and Wt in 203 urban and 355 rural children, aged 3 to 9 y, from North West Syria, assigning the categorical BMI classifications of the CDC and IOTF, as well as CDC HAZ scores. Results: 39.9% of urban children were classified beyond the 85 th percentile (21.7% risk of overweight, 18.2% overweight) by the CDC classification, whereas 29.6% had excess weight as categorized by the IOTF standard (21.7% overweight and 7.9% obese). For rural children, differential classification by standard become more evident, with 11.8% with excess weight (10.1% at risk and 1.7% overweight) using the CDC and 6.5% with excess weight (6.2% overweight and 0.3% obese) using the IOTF BMI charts, respectively. The interaction between HAZ and BMI Z‐score is positive in the combined sample (r 2 =0.198 p=0.01). Conclusion: In children, different BMI standards differ in diagnostic classification, but converge in biological analysis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".