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Record W3015338373 · doi:10.1002/eat.23274

First presentation of restrictive early onset eating disorders in Asian children

2020· article· en· W3015338373 on OpenAlexaff
Chu Shan Elaine Chew, Siobhán Kelly, Amerie Baeg, Jean Yin Oh, Kumudhini Rajasegaran, Courtney Davis

Bibliographic record

VenueInternational Journal of Eating Disorders · 2020
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsPediatricsEating disordersMedicineMalnutritionAnorexia nervosaWeight lossPresentation (obstetrics)Age of onsetRetrospective cohort studyObesityPsychiatryInternal medicineSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aims to describe the spectrum of children with restrictive early onset eating disorders (EOEDs), defined as below 13 years of age, presenting to a tertiary institution in Asia and comparing them with older adolescents with eating disorders. METHODS: This is a retrospective case review of Asian children who were treated in an eating disorder center. Baseline characteristics and inpatient management at first presentation of children younger than 13 years of age (EOED) were compared to those in older adolescents. RESULTS: A total of 288 patients with restrictive eating disorders were analyzed with 53 (18%) patients having onset younger than age 13 at initial presentation. There were no significant differences in percentage weight loss and hospitalization rates between the two age groups. Patients with EOED presented with significantly shorter duration of symptoms, and lower rates of secondary amenorrhea. More patients with EOED required phosphate supplementation compared to those in older age group. CONCLUSION: Despite having a shorter duration of illness, Asian children with EOED had similar percentage weight loss and rates of admission due to malnutrition as those in older Asian adolescent patients. This study underlined the severity of EOEDs and the need for early recognition and medical assessment.

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 score0.019
Threshold uncertainty score0.808

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 science0.0010.000
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 head0.015
GPT teacher head0.314
Teacher spread0.299 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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