Continuous cardiometabolic risk score definitions in early childhood: a scoping review
Bibliographic record
Abstract
BACKGROUND: Cardiometabolic risk (CMR) in young children has been measured using various approaches, including a continuous summary score that incorporates components such as adiposity, lipids, metabolic factors and blood pressure. OBJECTIVES: The objective of this study was to comprehensively review definitions of continuous CMR scores in children <10 years of age. METHODS: A scoping review was conducted using a systematic search of four scientific databases up to June 2016. Inclusion criteria were children <10 years of age and report of a continuous CMR score. RESULTS: Ninety-one articles were included. Most studies were published from 2007 to 2016 (96%). Nearly all continuous CMR scores (90%) were calculated using the sum or the mean of z-scores, and many articles age-standardized and sex-standardized components within their own population. The mean number of variables included in the risk scores was 5 with a range of 3-11. The most commonly included score components were waist circumference (52%), triglycerides (87%), high-density lipoprotein cholesterol (67%), glucose (43%) and systolic blood pressure (52%). IMPORTANCE: Continuous CMR scores are emerging frequently in the child health literature and are calculated using numerous methods with diverse components. This heterogeneity limits comparability across studies. A harmonized definition of CMR in childhood is needed.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".