MétaCan
Menu
Back to cohort
Record W2514398939 · doi:10.1002/eat.22602

Severe avoidant/restrictive food intake disorder and coexisting stimulant treated attention deficit hyperactivity disorder

2016· article· en· W2514398939 on OpenAlexaff
Alexandra Pennell, Jennifer Couturier, Christina Grant, Natasha Johnson

Bibliographic record

VenueInternational Journal of Eating Disorders · 2016
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsMcMaster University
Fundersnot available
KeywordsStimulantAppetitePsychologyPsychiatryAttention deficit hyperactivity disorderEating disordersAttention deficit disorderPoor AppetiteClinical psychologyMedicine

Abstract

fetched live from OpenAlex

There is a growing body of literature describing the development, clinical course, and treatment of avoidant/restrictive food intake disorder (ARFID), a diagnostic category introduced in the DSM-5. However, information surrounding complex cases of ARFID involving coexisting medical and/or psychiatric disorders remains scarce. Here we report on two cases of young patients diagnosed concurrently with ARFID and attention deficit hyperactivity disorder (ADHD) who both experienced significant growth restriction following initiation of stimulant medication. The appetite suppressant effect of stimulants exacerbated longstanding avoidant and restrictive eating behaviors resulting in growth restriction and admission to an inpatient eating disorders unit. The implications of ARFID exacerbated by stimulant-treated ADHD are explored, as well as the treatment delivered. These cases suggest that further research is needed to explore management options to counteract the appetite suppression effects of stimulants, while simultaneously addressing attention deficit symptoms and oppositional behavior. © 2016 Wiley Periodicals, Inc. (Int J Eat Disord 2016; 49:1036-1039).

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)0.001
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 score0.144
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.020
GPT teacher head0.307
Teacher spread0.287 · 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

Citations42
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Eating DisordersSame topicEating Disorders and BehaviorsFrench-language works237,207