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Record W4391317401 · doi:10.1186/s40337-023-00958-x

Avoidant/restrictive food intake disorder differs from anorexia nervosa in delay discounting

2024· article· en· W4391317401 on OpenAlexaff
Casey M. Stern, Iman McPherson, Melissa J. Dreier, Kathryn Coniglio, Lilian P. Palmer, Julia Gydus, Haley Graver, Laura Germine, Nassim Tabri, Shirley B. Wang, Lauren Breithaupt, Kamryn T. Eddy, Jennifer J. Thomas, Franziska Plessow, Kendra R. Becker

Bibliographic record

VenueJournal of Eating Disorders · 2024
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsCarleton University
FundersNational Institute of Mental HealthCharles A. King Trust
KeywordsDelay discountingImpulsivityDiscountingCognitionPsychologyTemporal discountingEating disordersAnorexia nervosaDevelopmental psychologyBulimia nervosaClinical psychologyPsychiatryEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Avoidant/restrictive food intake disorder (ARFID) and anorexia nervosa (AN) are the two primary restrictive eating disorders; however, they are driven by differing motives for inadequate dietary intake. Despite overlap in restrictive eating behaviors and subsequent malnutrition, it remains unknown if ARFID and AN also share commonalities in their cognitive profiles, with cognitive alterations being a key identifier of AN. Discounting the present value of future outcomes with increasing delay to their expected receipt represents a core cognitive process guiding human decision-making. A hallmark cognitive characteristic of individuals with AN (vs. healthy controls [HC]) is reduced discounting of future outcomes, resulting in reduced impulsivity and higher likelihood of favoring delayed gratification. Whether individuals with ARFID display a similar reduction in delay discounting as those with AN (vs. an opposing bias towards increased delay discounting or no bias) is important in informing transdiagnostic versus disorder-specific cognitive characteristics and optimizing future intervention strategies. METHOD: To address this research question, 104 participants (ARFID: n = 57, AN: n = 28, HC: n = 19) completed a computerized Delay Discounting Task. Groups were compared by their delay discounting parameter (ln)k. RESULTS: Individuals with ARFID displayed a larger delay discounting parameter than those with AN, indicating steeper delay discounting (M ± SD = -6.10 ± 2.00 vs. -7.26 ± 1.73, p = 0.026 [age-adjusted], Hedges' g = 0.59), with no difference from HC (p = 0.514, Hedges' g = -0.35). CONCLUSION: Our findings provide a first indication of distinct cognitive profiles among the two primary restrictive eating disorders. The present results, together with future research spanning additional cognitive domains and including larger and more diverse samples of individuals with ARFID (vs. AN), will contribute to identifying maintenance mechanisms that are unique to each disorder as well as contribute to the optimization and tailoring of treatment strategies across the spectrum of restrictive eating disorders.

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)0.002
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.291
Teacher spread0.277 · 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

Citations12
Published2024
Admission routes1
Has abstractyes

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