Obesity estimates for children based on parent-reported versus direct measures.
Bibliographic record
Abstract
BACKGROUND: Studies based on adolescents and adults have found that the use of self-reported height and weight to calculate body mass index (BMI) yields a lower prevalence of obesity than do estimates based on measured data. Relatively few studies have examined the bias resulting from the use of parent-reported height and weight for children, and the findings have been inconsistent. DATA AND METHODS: Data are from the 2007 to 2009 Canadian Health Measures Survey. Parent-reported height and weight of children aged 6 to 11 (n=854) were obtained. Subsequently, the children's height and weight were directly measured. RESULTS: On average, parents underestimated the height (3.3 cm) and weight (1.1 kg) of their children. Estimates of the prevalence of obesity were significantly higher when based on parent-reported versus measured values for children aged 6 to 8; the two collection methods yielded similar estimates of obesity for children aged 9 to 11. For children in both age groups, misclassification errors for BMI categories were substantial when based on parent-reported values. This weakened associations between obesity and health indicators such as aerobic fitness and systolic blood pressure. The variance explained by factors associated with the bias in parent-reported height and weight was small, particularly for height. The use of correction equations based on variables associated with the bias resulted in a very modest reduction in misclassification errors. INTERPRETATION: Bias associated with parental reports of children's height and weight results in misclassification errors for obesity that affect relationships with other variables. Efforts to establish correction equations to adjust for this bias were unsuccessful. Direct measures are required to accurately calculate obesity estimates and their relationships with health indicators in 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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.003 | 0.001 |
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".