Parental hypercholesterolemia and family medical history as predictors of hypercholesterolemia in their children
Bibliographic record
Abstract
INTRODUCTION: Parental hypercholesterolemia would be a better predictor of hypercholesterolemia than family medical history in children. OBJECTIVES: To compare the strength of association and predictive values of parental hypercholesterolemia versus a positive family history in pediatric hypercholesterolemia. Material and methods. Cross-sectional, analytical study. Cholesterol levels were measured in children aged ≥ 6 and < 12 years and in their biological parents. A survey was administered to parents. The association was estimated using the odds ratio (OR), and its predictive value was determined. The relationship between hypercholesterolemia in parents and their children was studied with multilevel regression. RESULTS: A total of 332 children, 304 mothers, and 206 fathers were assessed. A cholesterol level ≥ 240 mg/dL in one or both parents and ≥ 200 mg/dL in children showed: OR= 6.40; 95 % confidence interval (CI)= 2.85-14.48; p < 0.0001; sensitivity= 69 %; specihcity= 74 %; positive predictive value (PPV)= 34 %; negative predictive value (NPV)= 93 %; positive likelihood ratio (LR+)= 2.69; negative likelihood ratio (LR-)= 0.42. Family medical history versus children with cholesterol level ≥ 200 showed: OR= 1.86; 95 % CI= 0.84-4.11; p= 0.1272; sensitivity= 69 %; specihcity= 46 %; PPV= 19 %; NPV= 89 %; LR+= 1.27; LR-= 0.68. Cholesterol was 2.9 and 2.5 mg/dL higher per every 10 mg/dL of increased cholesterol in mothers and fathers, respectively. CONCLUSIONS: Parental hypercholesterolemia was significantly associated with hypercholesterolemia in children and showed a higher predictive power than a positive family medical history.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".