Misclassification of child body mass index from cut-points defined by rounded percentiles instead of Z-scores
Bibliographic record
Abstract
OBJECTIVE: To evaluate the misclassification resulting from the use of body mass index (BMI) cut-points defined by rounded percentiles instead of Z-scores in early childhood. Using data from the TARGet Kids primary care network we conducted a cross-sectional study among 5836 children < 6 years of age. The World Health Organization growth standards were used to calculate BMI-for-age Z-scores. BMI Z-score cut-points of < - 2.0, > 1.0, > 2.0, > 3.0 are recommended to define wasted, at risk of overweight, overweight and obese. However, rounded percentiles of the 3rd, 85th, 97th, and 99.9th are commonly used. Misclassification was calculated comparing the frequency distributions for BMI categories defined by rounded percentiles and Z-score cut-points. RESULTS: Using rounded percentiles, the proportion of children who were wasted, at risk of overweight, overweight, and obese was 4.2, 12.5, 4.3 and 0.8%, whereas the distribution using Z-scores was: 3.6, 13.8, 3.4 and 1.0%, respectively. Overall, 117 (2%) children were misclassified when using percentiles instead of Z-scores; however, 13% (33/245) of children who were wasted and 14% (8/57) of children who were obese were misclassified. Misclassification of child growth results from the use of cut-points defined by rounded percentiles instead of Z-scores and limits comparability between studies. Trial registration Clinicaltrials.gov NCT01869530 June 5, 2013.
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".