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Record W2913309074 · doi:10.1002/eat.23021

Avoidant restrictive food intake disorder: First do no harm

2019· article· en· W2913309074 on OpenAlexaff
Debra K. Katzman, Mark L. Norris, Nancy Zucker

Bibliographic record

VenueInternational Journal of Eating Disorders · 2019
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsChildren's Hospital of Eastern OntarioAgricultural Research Institute of OntarioUniversity of TorontoSickKids FoundationUniversity of OttawaHospital for Sick Children
FundersNational Institute of Mental Health
KeywordsAnorexia nervosaHarmPsychologyFood intakePsychiatryDevelopmental psychologyEating disordersPsychotherapistClinical psychologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: This opinion piece offers some considerations, both medical and psychological, for the use of nasogastric tube (NGT) feedings in the treatment of avoidant restrictive food intake disorder (ARFID) in children and adolescents. METHOD: Although there is empirical support for the use of NGT feedings in the treatment of anorexia nervosa, this evidence base does not exist for the treatment of ARFID. As such, there is need to delineate pragmatic considerations in the use of this procedure. RESULTS: Issues of medical necessity notwithstanding, we advise that the use of this procedure be considered more cautiously due to the oral sensitivities inherent in many individuals with ARFID and the potential psychological consequences. These sensitivities may make the experience of NGT feedings particularly aversive, with the potential of creating iatrogenic conditioned food aversions. DISCUSSION: This article encourages clinicians to give careful thought and attention when considering NGT feedings in children and adolescents with ARFID.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0050.005
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 head0.009
GPT teacher head0.267
Teacher spread0.258 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations21
Published2019
Admission routes1
Has abstractyes

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