Determining cardiovascular disease risk in elementary school children: developing a healthy heart score.
Bibliographic record
Abstract
At least 50% of children have one or more cardiovascular disease (CVD) risk factor. We aimed to 1) determine the prevalence of CVD risk factors in a sample of Canadian children, and 2) create a Healthy Heart Score that could be used in a school setting, to identify children with a greater number and severity of CVD risk factors. Children (n = 242, 122M, 120F, aged 9-11 years) were assessed for cardiovascular fitness, physical activity, systolic/diastolic blood pressure, and body mass index (BMI). Biological values were converted to age and sex specific percentiles and allocated a score. Healthy Heart Scores could range between 5 and 18, with lower scores suggesting a healthier cardiovascular profile. Seventy-seven children volunteered for blood samples in order to assess the relationship between the Healthy Heart Score and (total cholesterol (TC), high and low-density lipoprotein cholesterol (HDL, LDL) and triglycerides (TG). Fifty eight percent of children had elevated scores for at least 1 risk factor. The group mean Healthy Heart Score was 8 (2.2). The mean score was significantly higher in boys (9 (2.2)) compared with girls (8 (2.1), p < 0.01). A high score was significantly associated with a low serum HDL, a high TC:HDL and a high TG concentration. Our results support other studies showing a high prevalence of CVD risk factors in children. Our method of allocation of risk score, according to percentile, allows for creation of an age and sex specific CVD risk profile in children, which takes into account the severity of the elevated risk factor. Key pointsThere was a high incidence of elevated risk factors for cardiovascular disease in Canadian elementary school children.Physical fitness and physical activity levels were particularly low.In this cohort, boys had increased levels of cardiovascular disease risk factors compared with age-matched girls.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".