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Record W2913471539 · doi:10.1002/eat.23042

Radcliffe ARFID Workgroup: Toward operationalization of research diagnostic criteria and directions for the field

2019· article· en· W2913471539 on OpenAlexaff
Kamryn T. Eddy, Stephanie G. Harshman, Kendra R. Becker, Elana M. Bern, Rachel Bryant‐Waugh, Anja Hilbert, Debra K. Katzman, Elizabeth A. Lawson, Laurie D. Manzo, Jessie E. Menzel, Nadia Micali, Rollyn M. Ornstein, Sarah T. Sally, Sharon P. Serinsky, William G. Sharp, Kathryn H. Stubbs, B. Timothy Walsh, Hana F. Zickgraf, Nancy Zucker, Jennifer J. Thomas

Bibliographic record

VenueInternational Journal of Eating Disorders · 2019
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Mental HealthRadcliffe Institute for Advanced Study, Harvard University
KeywordsWorkgroupOperationalizationField (mathematics)PsychologyEpistemologyComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

OBJECTIVE: Since its introduction to the psychiatric nomenclature in 2013, research on avoidant/restrictive food intake disorder (ARFID) has proliferated highlighting lack of clarity in how ARFID is defined. METHOD: In September 2018, a small multi-disciplinary pool of international experts in feeding disorder and eating disorder clinical practice and research convened as the Radcliffe ARFID workgroup to consider operationalization of DSM-5 ARFID diagnostic criteria to guide research in this disorder. RESULTS: By consensus of the Radcliffe ARFID workgroup, ARFID eating is characterized by food avoidance and/or restriction, involving limited volume and/or variety associated with one or more of the following: weight loss or faltering growth (e.g., defined as in anorexia nervosa, or by crossing weight/growth percentiles); nutritional deficiencies (defined by laboratory assay or dietary recall); dependence on tube feeding or nutritional supplements (≥50% of daily caloric intake or any tube feeding not required by a concurrent medical condition); and/or psychosocial impairment. CONCLUSIONS: This article offers definitions on how best to operationalize ARFID criteria and assessment thereof to be tested in existing clinical populations and to guide future study to advance understanding and treatment of this heterogeneous disorder.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.058
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0010.000

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 head0.046
GPT teacher head0.443
Teacher spread0.397 · 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

Citations95
Published2019
Admission routes1
Has abstractyes

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