Accuracy of the Weight-for-age Index in Identifying Obese Children in the Emergency Setting
Bibliographic record
Abstract
Obesity has been defined as weight-for-age > 95 th percentile for research/clinical settings like the emergency department where height is not routinely available. Our main objective was to determine the sensitivity of weight-for-age > 95% th percentile in identifying obesity, using Body Mass Index (BMI)-for-age > 95% percentile as the reference standard. We also determined the specificity, and predictive values, and the correlation between weight and BMI-for-age values. This was a cross-sectional study with prospectively collected data conducted at two urban, tertiary care pediatric emergency departments in Canada. Children between 2 and 17 years of age with acute extremity injuries were enrolled. Of the 2259 participants, 1283 (56.1%) were male and the mean (SD) age was 9.5 (4.1) years. Using weight-for-age > 95 th percentile, 326 [14.3% (12.9, 15.7]) were classified as obese, while using BMI-for-age > 95 th percentile criteria determined that 363 [16.1% (14.6, 17.7)] were obese, p 95 th percentile to identify obesity was 62.2% (57.0, 67.2). The specificity, positive and negative predictive values were 94.5 % (93.6, 95.6), 14.4% (13.0, 16.0), and 85.6% (84.0, 87.0), respectively. The correlation between BMI and weight-for-age was 0.71 (0.68, 0.73). Although there may be limited validity in using this weight-for-age cut-off as a definition for obesity in research that includes children with acute injuries, the high specificity may provide some clinical utility in identifying two-thirds of children who are obese while minimizing false labeling of this condition.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".