Accuracy of body mass index for age to diagnose obesity in Mexican schoolchildren.
Bibliographic record
Abstract
OBJECTIVE: To compare the accuracy of three BMI-forage references (World Health Organization reference, WHO; the updated International Obesity Task Force reference, IOTF; and Centers for Disease Control and Prevention (CDC) growth charts) to diagnose obesity in Mexican children. METHODS: A convenience sample of Mexican schoolchildren (n = 218) was assessed. The gold standard was the percentage of body fat estimated by deuterium dilution technique. Sensitivity and specificity of the classical cutoff point of BMI-for-age to identify obesity (i.e. > 2.00 standard deviation, SD) were estimated. The accuracy (i.e. area under the curve, AUC) of three BMI-for-age references for the diagnosis of obesity was estimated with the receiver operating characteristic (ROC) curves method. The optimal cutoff point (OCP) was determined. RESULTS: The cutoff points to identify obesity had low (WHO reference: 57.6%, CDC: 53.5%) to very low (IOTF reference: 40.4%) sensitivities, but adequate specificities (91.6%, 95.0%, and, 97.5%, respectively). The AUC of the three references were adequate (0.89). For the IOTF reference, the AUC was lower among the older children. The OCP for the CDC reference (1.24 SD) was lower than the OCP for WHO (1.53 SD) and IOTF charts (1.47 SD). CONCLUSIONS: The classical cutoff point for obesity has low sensitivity--especially for the IOTF reference. The accuracy of the three references was similar. However, to obtain comparable diagnosis of obesity different cutoff points should be used depending of the reference.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".