The Healthy Eating Assessment Tool (HEAT): A Simplified 10-Point Assessment of CHILD-2 Dietary Compliance for Children and Adolescents with Dyslipidemia
Bibliographic record
Abstract
Traditional dietary assessment tools used to determine achievement of cholesterol-lowering dietary targets, defined in the Cardiovascular Health Integrated Lifestyle Diet (CHILD-2), are time intensive. We sought to determine the utility of the Healthy Eating Assessment Tool (HEAT), a simplified 10-point dietary assessment tool, in relation to meeting dietary cut points of the CHILD-2, as well as its association with markers of adiposity and lipid variables. We performed a 2-year single-center, prospective cross-sectional study of pediatric patients with dyslipidemia. HEAT score associations with meeting CHILD-2 fat targets were modest. Only patients with the highest HEAT scores (good 43%, excellent 64%) met the CHILD-2 cut point of <25% total fat calories (p = 0.03), with a non-significant trend for limiting the percentage of daily saturated fat to <8% (excellent 64%), and no association with cholesterol intake. There were more consistent associations with markers of adiposity (body mass index z-score r = −0.31, p = <0.01 and waist-to-height ratio r = −0.31, p = <0.01), and there was no independent association with lipid levels. While fat-restricted diets are safe, they are not particularly effective for treatment of dyslipidemia or for weight management alone. The HEAT may be a more useful and simplified way of assessing and tracking broader dietary goals in clinical practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".