Validity of self-reported height and weight for measuring prevalence of obesity.
Bibliographic record
Abstract
OBJECTIVES: To examine the validity of self-reported body mass index (BMI) in estimating the prevalence of obesity in the Canadian population, and to suggest a model for predicting actual BMI from self-reported data. METHODS: This analysis is based on 1131 participants with both self-reported and measured height and weight from the Canadian Community Health Survey, Cycle 2.2 dataset. We estimated the prevalence of obesity as well as the mean and standard deviation (SD) of BMI according to sex, age group, and measured weight classification. Multiple regression analysis was used to build a model to assess the relation between actual BMI and variables of age, sex, and self-reported BMI. RESULTS: The overall prevalence of obesity was 23.0% based on measured BMI, and 15.6% based on self-reported BMI. Estimated mean (SD) for self-reported and measured BMI were 25.8 (4.8) and 26.9 (5.0) kg/m(2), respectively. Only 74.3% of obese men and 56.2% of obese women were correctly classified as obese on the basis of self-reported measures. Females and heavier respondents showed more BMI under-reporting than others. CONCLUSIONS: To estimate overweight and obesity in etiological and disease relationship studies, the use of measured height and weight in BMI estimation is preferable to the use of self-reported values. However, if self-reported height and weight are used in population studies, our proposed model can be used to reliably predict the actual BMI with a narrow 95% confidence interval.
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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 | 0.020 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".