Distribution of Lipoproteins by Age and Gender in Adolescents
Bibliographic record
Abstract
BACKGROUND: The current National Cholesterol Education Program lipoprotein classification system for children and adolescents is recommended for use among 2- to 19-year-olds. This classification system does not take into account gender differences or the natural fluctuations in lipoprotein concentrations that occur with growth and maturation. METHODS AND RESULTS: Data from the National Health and Nutrition Examination Surveys were used to develop age- and gender-specific thresholds that can be used to denote abnormal levels of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides. Each curve was linked to the adult National Cholesterol Education Program Adult Treatment Panel III lipoprotein thresholds using LMS (Lambda-Mu-Sigma) growth curve regression methods. A series of growth curves and tables are presented that can be used to diagnose high-risk lipoprotein levels in the clinical and research settings. For example, in 1-year increments for males starting at age 12 and extending to age 19 years, the high-risk thresholds for total cholesterol were 6.03, 5.83, 5.70, 5.70, 5.77, 5.88, 6.02, and 6.16 mmol/L. The corresponding high-risk threshold for adults (> or = 20 years) is 6.22 mmol/L. CONCLUSIONS: The present study is the first attempt at developing age- and gender-specific lipoprotein threshold concentrations for adolescents. This new classification system should provide a more accurate diagnosis of high-risk lipoprotein levels and associated cardiovascular risks in adolescents.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".