MétaCan
Menu
Back to cohort
Record W2128934621 · doi:10.1002/eat.20274

Does the frequency of anxiety and depressive disorders differ between diagnostic subtypes of anorexia nervosa and bulimia?

2006· article· en· W2128934621 on OpenAlexaff
Nathalie Godart, Sylvie Berthoz, Zoé Rein, Fabienne Perdereau, François Lang, Jean-Luc Vénisse, Olivier Halfon, P Bízouard, Gwenolé Loas, Maurice Corcos, Philippe Jeammet, Martine F. Flament, Florence Curt

Bibliographic record

VenueInternational Journal of Eating Disorders · 2006
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsAnorexia nervosaAnorexiaAnxietyPsychologyBulimia nervosaClinical psychologyEating disordersDepressive symptomsPsychiatryMedicineInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of the present work is to determine whether the prevalence of depressive and anxiety disorders varies in subgroups of eating disorders (ED) according to age, ED duration, mode of care provision, and body mass index (BMI). METHOD: Using the Mini International Neuropsychiatric Interview (MINI), the frequency of anxiety and depressive disorders was evaluated in 271 ED participants. Their prevalence was compared in subgroups of anorexics (AN-R and AN-BN) and bulimics (BN), both before and after controlling for potential confounding variables. RESULTS: Current or lifetime comorbidity of anxiety and depressive disorders did not differ between AN-R and AN-BN groups. Social phobia, panic disorders, and obsessive-compulsive disorder (OCD) were significantly more frequent in AN-BN and AN-R groups. Panic disorder was more frequent in the BN group. CONCLUSION: Several confounding factors, in particular those identified in the present study, may explain previous conflicting results on the frequency of anxiety and depressive disorders in ED. Nevertheless, the study confirmed that OCD is more frequent in AN, even after controlling for confounding factors.

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)0.000
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 score0.020
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.007
GPT teacher head0.274
Teacher spread0.266 · 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

Citations71
Published2006
Admission routes1
Has abstractyes

Explore more

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