Creation of a reference dataset of neck sizes in children: standardizing a potential new tool for prediction of obesity-associated diseases?
Bibliographic record
Abstract
BACKGROUND: Neck circumference (NC), is an emerging marker of obesity and associated disease risk, but is challenging to use as a screening tool in children, as age and sex standardized cutoffs have not been determined. A population-based sample of NC in Canadian children was collected, and age- and sex-specific reference curves for NC were developed. METHODS: NC, waist circumference (WC), weight and height were measured on participants aged 6-17 years in cycle 2 of the Canadian Health Measures Survey. Quantile regression of NC versus age in males and females was used to obtain NC percentiles. Linear regression was used to examine association between NC, body mass index (BMI) and WC. NC was compared in healthy weight (BMI < 85th percentile) and overweight/obese (BMI > 85th percentile) subjects. RESULTS: The sample included 936 females and 977 males. For all age and sex groups, NC was larger in overweight/obese children (p < 0.0001). For each additional unit of BMI, average NC in males was 0.49 cm higher and in females, 0.43 cm higher. For each additional cm of WC, average NC in males was 0.18 cm higher and in females, 0.17 cm higher. CONCLUSION: This study presents the first reference data on Canadian children's NC. The reference curves may have future clinical applicability in identifying children at risk of central obesity-associated conditions and thresholds associated with disease risk.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".