Clinical Research Cardiovascular Risk-Factor Profiles of Normal and Overweight Children and Adolescents: Insights From the Canadian Health Measures Survey
Bibliographic record
Abstract
Background: There is no cardiovascular disease (CVD) risk factor profile in a representative sample of Canadian children and adolescents according to weight status. The 2007-2009 Canadian Health Measures Survey, launched by Statistics Canada in partnership with Health Canada and the Public Health Agency of Canada, provides an opportunity to address this gap. Methods: The Canadian Health Measures Survey collected information at 15 sites across Canada from March 2007 to March 2009 from Canadians aged 6 to 79 years living in private households. The survey consisted of a household interview and a visit to a mobile examination centre to perform physical measurements, including anthropometry, blood pressure, and biospecimen collection. The present analysis is based on data from 2087 children and adolescents aged 6 to 19 years. Results: Childrenandadolescentswhowereoverweightorobesehadon average higher mean concentrations and higher prevalence of adverse levels of CVD risk factors (systolic and diastolic blood pressure, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, apolipoprotein B, C-reactive protein, homocysteine, and insulin levels) than did normal-weight children and adolescents. Adjustment for covariates (gender, age, household education,household income adequacy, and provinceof residence) and
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".