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Record W2266782139 · doi:10.2147/ndt.s82538

Update on eating disorders: current perspectives on avoidant/restrictive food intake disorder in children and youth

2016· review· en· W2266782139 on OpenAlexaff
Mark C. Norris, Wendy Spettigue, Debra K. Katzman

Bibliographic record

VenueNeuropsychiatric Disease and Treatment · 2016
Typereview
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsSickKids FoundationUniversity of TorontoChildren's Hospital of Eastern OntarioHospital for Sick ChildrenUniversity of Ottawa
Fundersnot available
KeywordsMedicineEating disordersPresentation (obstetrics)PsychiatryFood intakeCohortClinical psychologyPediatrics

Abstract

fetched live from OpenAlex

Avoidant/restrictive food intake disorder (ARFID) is a new eating disorder diagnosis that was introduced in the Diagnostic and Statistical Manual of Mental Disorders (DSM) fifth edition. The fourth edition of the DSM had failed to adequately capture a cohort of children, adolescents, and adults who are unable to meet appropriate nutritional and/or energy needs, for reasons other than drive for thinness, leading to significant medical and/or psychological sequelae. With the introduction of ARFID, researchers are now starting to better understand the presentation, clinical characteristics, and complexities of this disorder. This article outlines the diagnostic criteria for ARFID with specific focus on children and youth. A case example of a patient with ARFID, factors that differentiate ARFID from picky eating, and the estimated prevalence in pediatric populations are discussed, as well as clinical and treatment challenges that impact health care providers providing treatment for patients.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.006
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.022
GPT teacher head0.313
Teacher spread0.291 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations169
Published2016
Admission routes1
Has abstractyes

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