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Record W2166606637 · doi:10.5214/ans.0972.7531.200409

A critique of the literature on etiology of eating disorders

2013· review· en· W2166606637 on OpenAlexaff
Azadeh A Rikani, Zia Choudhry, Adnan Maqsood Choudhry, Huma Ikram, Muhammad Waheed Asghar, Dilkash Kajal, Abdül Waheed, Nusrat Jahan Mobassarah

Bibliographic record

VenueAnnals of Neurosciences · 2013
Typereview
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of OttawaUniversity of AlbertaMcGill University Health CentreHealth Research FoundationMcGill UniversityDouglas Mental Health University InstituteDouglas College
Fundersnot available
KeywordsEating disordersBulimia nervosaAnorexia nervosaEtiologyBinge eatingBinge-eating disorderAffect (linguistics)PsychiatryAnorexiaPsychologySociocultural evolutionMedicineClinical psychologyPathology

Abstract

fetched live from OpenAlex

The development of eating disorders including anorexia nervosa, bulimia nervosa, binge eating disorder, and atypical eating disorders that affect many young women and even men in the productive period of their lives is complex and varied. While numbers of presumed risk factors contributing to the development of eating disorders are increasing, previous evidence for biological, psychological, developmental, and sociocultural effects on the development of eating disorders have not been conclusive. Despite the fact that a huge body of research has carefully examined the possible risk factors associated with the eating disorders, they have failed not only to uncover the exact etiology of eating disorders, but also to understand the interaction between different causes of eating disorders. This failure may be due complexities of eating disorders, limitations of the studies or combination of two factors. In this review, some risk factors including biological, psychological, developmental, and sociocultural are discussed.

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)0.011
metaresearch head score (Gemma)0.021
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0070.008
Science and technology studies0.0010.008
Scholarly communication0.0040.007
Open science0.0040.003
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0030.002

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 head0.144
GPT teacher head0.461
Teacher spread0.317 · 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
GenreReview

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

Citations224
Published2013
Admission routes1
Has abstractyes

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