Usefulness of the American Academy of Pediatrics Recommendations for Identifying Youths With Hypercholesterolemia
Bibliographic record
Abstract
OBJECTIVE: To determine the usefulness of parent history of hypercholesterolemia and cardiovascular disease as a screening criterion for hypercholesterolemia in youths. METHODS: Data were available from a population-based survey of 3665 Quebec youths aged 9, 13, and 16 years (81.2% of eligible subjects). Blood specimens were collected from 2475 subjects (54.8% of those eligible), and questionnaire data were obtained from 3048 parents (67.5% of those eligible). Lipids were measured in a Centers for Disease Control and Prevention standardized laboratory. Usefulness of parent history in identifying borderline/high low-density lipoprotein cholesterol (LDL-C) (> or =2.8 mmol/L [> or =110 mg/dL]) and high LDL-C (> or =3.4 mmol/L [> or =130 mg/dL]) was assessed according to test performance statistics (sensitivity, specificity, positive predictive value, and negative predictive value). RESULTS: The prevalence of a positive parent history was 25.6%; 18.3% of subjects had borderline/high LDL-C, and 4.8% had high LDL-C. Sensitivity, specificity, positive predictive value, and negative predictive value of parent history were 33.1%, 76.0%, 23.7%, and 83.5%, respectively, for identifying borderline/high LDL-C; they were 40.7%, 75.1%, 7.7%, and 96.1% for identifying high LDL-C. Test performance statistics were not improved in subgroups defined according to age, gender, parent education, household income, family status, and family origin (French Canadian, other); neither were they improved by adding screening criteria (parent history of diabetes or hypertension, or youth overweight). CONCLUSION: Parent history screening criteria offer little improvement over random population screening in identifying youths with hypercholesterolemia.
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".