Mid‐upper arm circumference as a screening tool for identifying children with obesity: a 12‐country study
Bibliographic record
Abstract
BACKGROUND: No studies have examined if mid-upper arm circumference (MUAC) can be an alternative screening tool for obesity in an international sample of children differing widely in levels of human development. OBJECTIVE: Our aim is to determine whether MUAC could be used to identify obesity in children from 12 countries in five major geographic regions of the world. METHODS: This observational, multinational cross-sectional study included 7337 children aged 9-11 years. Anthropometric measurements were objectively assessed, and obesity was defined according to the World Health Organization reference data. RESULTS: In the total sample, MUAC was strongly correlated with adiposity indicators in both boys and girls (r > 0.86, p < 0.001). The accuracy level of MUAC for identifying obesity was high in both sexes and across study sites (overall area under the curve of 0.97, sensitivity of 95% and specificity of 90%). The MUAC cut-off value to identify obesity was ~25 cm for both boys and girls. In country-specific analyses, the cut-off value to identify obesity ranged from 23.2 cm (boys in South Africa) to 26.2 cm (girls in the UK). CONCLUSIONS: Results from this 12-country study suggest that MUAC is a simple and accurate measurement that may be used to identify obesity in children aged 9-11 years. MUAC may be a promising screening tool for obesity in resource-limited settings.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".