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Distribution of Lipoproteins by Age and Gender in Adolescents

2006· article· en· W1968767907 on OpenAlexafffund
Courtney J. Jolliffe, Ian Janssen

Bibliographic record

VenueCirculation · 2006
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsQueen's University
FundersCanadian Institutes of Health ResearchHealth CanadaCenters for Disease Control and Prevention
KeywordsMedicineDistribution (mathematics)PhysiologyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.232
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations211
Published2006
Admission routes2
Has abstractyes

Explore more

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