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Record W2048616664 · doi:10.1002/eat.10261

Report of the National Institutes of Health workshop on overcoming barriers to treatment research in anorexia nervosa

2004· article· en· W2048616664 on OpenAlexaff
W. Stewart Agras, Harry Brandt, Cynthia M. Bulik, Regina Dolan‐Sewell, Christopher G. Fairburn, Katherine A. Halmi, David B. Herzog, David C. Jimerson, Allan S. Kaplan, Walter H. Kaye, Daniel Le Grange, James Lock, James E. Mitchell, Matthew V. Rudorfer, Linda L. Street, Ruth H. Striegel‐Moore, Kelly M. Vitousek, B. Timothy Walsh, Denise E. Wilfley

Bibliographic record

VenueInternational Journal of Eating Disorders · 2004
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsAnorexia nervosaPsychological interventionEating disordersSession (web analytics)PsychiatryPsychologyPsychotherapistMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Anorexia nervosa (AN) is associated with serious medical morbidity and has the highest mortality rate of all psychiatric disorders. The National Institutes of Health (NIH) Workshop on Overcoming Barriers to Treatment Research in Anorexia Nervosa convened on September 26-27, 2002 to address the dearth of treatment research in this area. The goals of this workshop were to discuss the stages of illness and illness severity, pharmacologic interventions, psychological interventions, and methodologic considerations. METHOD: The program consisted of a series of brief presentations by moderators, each followed by a discussion of the topic by workshop participants, facilitated by the session chair. RESULTS: This report summarizes the major discussions of these sessions and concludes with a set of recommendations related to the development of treatment research in AN based on these findings. DISCUSSION: It is crucial that treatment research in this area be prioritized.

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)0.069
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score0.366

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0690.033
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0100.002
Scholarly communication0.0060.003
Open science0.0050.013
Research integrity0.0120.021
Insufficient payload (model declined to judge)0.0120.003

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 head0.087
GPT teacher head0.446
Teacher spread0.359 · 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.

Study designNot applicable
DomainMethods
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

Citations168
Published2004
Admission routes1
Has abstractyes

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