Fat mass index performs best in monitoring management of obesity in prepubertal children
Bibliographic record
Abstract
OBJECTIVE: An early and accurate recognition of success in treating obesity may increase the compliance of obese children and their families to intervention programs. This observational, prospective study aimed to evaluate the ability and the time to detect a significant reduction of adiposity estimated by body mass index (BMI), percentage of fat mass (%FM), and fat mass index (FMI) during weight management in prepubertal obese children. METHODS: In a cohort of 60 prepubertal obese children aged 3-9 years included in an outpatient weight management program, BMI, %FM, and FMI were monitored monthly; the last two measurements were assessed using air displacement plethysmography. The outcome measures were the reduction of >5% of each indicator and the time to achieve it. RESULTS: The rate of detection of the outcome was 33.3% (95% CI: 25.9-41.6) using BMI, significantly lower (p<0.001) than either 63.3% using %FM (95% CI: 50.6-74.8) or 70.0% (95% CI: 57.5-80.1) using FMI. The median time to detect the outcome was 71 days using FMI, shorter than 88 days using %FM, and similar to 70 days using BMI. The agreement between the outcome detected by FMI and by %FM was high (kappa 0.701), but very low between the success detected by BMI and either FMI (kappa 0.231) or %FM (kappa 0.125). CONCLUSIONS: FMI achieved the best combination of ability and swiftness to identify reduction of adiposity during monitoring of weight management in prepubertal obese 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".