Validity of Self‐Reported Height and Weight to Derive BMI in Adults Participating in Special Olympics
Bibliographic record
Abstract
Abstract Individuals with intellectual disabilities (ID) experience high rates of overweight and obesity. Accurate height and weight data is needed to determine overweight and obesity, but direct measurement is not always feasible in large samples. In the general population, overweight and obesity prevalence studies have validated adult self‐reported height and weight, but this has not been done for adults with ID. The objectives of this study were to determine the validity of self‐reported height, weight, and derived body mass index (BMI), and to determine the diagnostic accuracy of derived BMI to identify overweight and obesity. Self‐reported height and weight were collected from 40 adult Special Olympics participants. The validity of self‐reported height and weight was determined by comparing them to measured height and weight. The differences between self‐reported and measured height, weight, and derived BMI were not significantly different; however, these findings were derived from results with wide confidence intervals. A high percentage of study participants correctly classified themselves as overweight/obese (sensitivity = 92.9%). This exploratory study encourages self‐reports and inclusion of individuals with ID in epidemiological studies, but requires further examination with a larger sample.
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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 | 0.007 | 0.023 |
| 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 | 0.000 |
| 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".