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Record W2098858824

Smoothed percentage body fat percentiles for U.S. children and adolescents, 1999-2004.

2011· article· en· W2098858824 on OpenAlexaff
Cynthia L. Ogden, Yan Li, David S. Freedman, Lori G. Borrud, Katherine M. Flegal

Bibliographic record

VenuePubMed · 2011
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPercentileMedicineBody mass indexClassification of obesityNational Health and Nutrition Examination SurveyBody fat percentageObesityDemographyChildhood obesityFat massPediatricsOverweightEndocrinologyPopulationEnvironmental healthStatisticsMathematics
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The high prevalence of obesity (defined by body mass index) among children and adolescents in the United States and elsewhere has prompted increased attention to body fat in childhood and adolescence. OBJECTIVE: This report provides smoothed estimates of major percentiles of percentage body fat for boys and girls aged 8-19 years in the United States. METHODS: Percentage body fat was obtained from whole-body, dual-energy x-ray absorptiometry (DXA) scans conducted during the 1999-2004 National Health and Nutrition Examination Survey. A nonparametric double-kernel method was employed to smooth percentile curves for the DXA data. RESULTS: The pattern of body fat development differs between boys and girls aged 8-19 years. In most age groups, girls have a higher percentage of body fat than boys. Among boys, there is a drop in body fat percentage in early adolescence that is especially pronounced at the higher percentiles. Among girls this pattern is not seen; percentage body fat increases slightly with age. CONCLUSIONS: These results provide a smoothed reference distribution of percentage body fat for U.S. children and adolescents aged 8-19 years.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.029
GPT teacher head0.236
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations127
Published2011
Admission routes1
Has abstractyes

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