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The Prevalence of Preadolescent Eating Disorders in the United States

2022· article· en· W4206950316 on OpenAlexaff
Stuart B. Murray, Kyle T. Ganson, Jonathan Chu, Kay Jann, Jason M. Nagata

Bibliographic record

VenueJournal of Adolescent Health · 2022
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Toronto
FundersNational Institute on Drug AbuseNational Heart, Lung, and Blood InstituteNational Institutes of HealthNational Institute of Mental HealthAmerican Heart Association
KeywordsSubclinical infectionBulimia nervosaAnorexia nervosaEating disordersMedicineBinge eatingPrevalencePsychiatryBinge-eating disorderCross-sectional studyPediatricsDemographyEpidemiologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: The prevalence of eating disorders (EDs) in young children remains relatively unknown. Here, we aimed to assess the prevalence of anorexia nervosa (AN), bulimia nervosa (BN), binge ED (BED), and their subclinical derivatives, among 10- to 11-year-old children in the United States. METHODS: Cross-sectional data from the year 1 sample of the nationwide Adolescent Cognitive Brain Development study were extracted, and unadjusted prevalence of EDs was reported, as per DSM-5 criteria. RESULTS: Among 10- to 11-year-old children in the United States, no cases of AN were reported. The prevalence of BN was negligible, whereas the prevalence of BED was 1.1%. The prevalence of subclinical AN, BN, and BED was 6%, 0.2%, and 0.5%, respectively. DISCUSSION: BED is the most prevalent ED subtype among preadolescent children in the United States, although subclinical markers for all ED subtypes are evident in this age range.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.078
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.347
Teacher spread0.321 · 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

Citations35
Published2022
Admission routes1
Has abstractno

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