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Record W2765317425 · doi:10.1159/000484525

An Evolutionary Genetic Perspective of Eating Disorders

2017· review· en· W2765317425 on OpenAlexaff
Alexandra Mayhew, Marie Pigeyre, Jennifer Couturier, David Meyre

Bibliographic record

VenueNeuroendocrinology · 2017
Typereview
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsBulimia nervosaBinge-eating disorderAnorexia nervosaEating disordersGeneticsPopulationGenetic predispositionPsychologyBinge eatingPhenotypeBiologyGenePsychiatryMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Eating disorders (ED) including anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED) affect up to 5% of the population in Western countries. Risk factors for developing an ED include personality traits, family environment, gender, age, ethnicity, and culture. Despite being moderately to highly heritable with estimates ranging from 28 to 83%, no genetic risk factors have been conclusively identified. Our objective was to explore evolutionary theories of EDs to provide a new perspective on research into novel biological mechanisms and genetic causes of EDs. We developed a framework that explains the possible interactions between genetic risk and cultural influences in the development of ED. The framework includes three genetic predisposition categories (people with mainly AN restrictive gene variants, people with mainly BED variants, and people with gene variants predisposing to both diseases) and a binary variable of either the presence or absence of pressure to be thin. We propose novel theories to explain the overlapping characteristics of the subtypes of AN (binge/purge and restrictive), BN, and BED. For instance, mutations/structural gene variants in the same gene causing opposite effects or mutations in nearby genes resulting in partial disequilibrium for the genes causing AN (restrictive) and BED may explain the overlap of phenotypes seen in AN (binge/purge).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.086
GPT teacher head0.430
Teacher spread0.344 · 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.

Study designOther design
Domainnot available
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

Citations46
Published2017
Admission routes1
Has abstractyes

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