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Record W4210843306 · doi:10.1177/10398562211067194

Anorexia nervosa, weight restoration and biological siblings: Differences and similarities in clinical characteristics

2022· article· en· W4210843306 on OpenAlexaff
Andrea Phillipou, Caroline Gurvich, David Castle, Susan L. Rossell

Bibliographic record

VenueAustralasian Psychiatry · 2022
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Health and Medical Research Council
KeywordsAnorexia nervosaPsychologyBody weightPsychiatryMedicineClinical psychologyDevelopmental psychologyPsychotherapistEating disordersInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Anorexia nervosa (AN) is associated with clinical characteristics including eating disorder symptomatology, negative mood states, perfectionism and cognitive inflexibility. Whether these characteristics differ across illness stages, and are also present in first-degree relatives, demonstrating heritability, is unclear. The aim of this research was to compare current AN (c-AN), weight-restored AN (wr-AN), sisters of individuals with AN (AN-sis) and healthy controls (HC) on these measures. METHOD: = 20/group) completed the study. RESULTS: Eating disorder symptomatology was similar among c-AN and wr-AN groups, whereas the AN-sis did not differ from either wr-AN or HC. Anxiety was significantly higher in c-AN, wr-AN and AN-sis groups, relative to HC. Increased perfectionism was identified in the c-AN and wr-AN groups compared to AN-sis and HC on the 'concern over mistakes', 'personal standards' and 'doubt and actions' subscales of the Multidimensional Perfectionism Scale. Group differences were not apparent on cognitive flexibility. CONCLUSIONS: These findings suggest that anxiety may be a risk factor or linked to genetic susceptibility for AN, as well as specific aspects of perfectionism that relate to self-imposed standards.

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 score0.004
Threshold uncertainty score0.600

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 integrity0.0000.001
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 head0.045
GPT teacher head0.337
Teacher spread0.292 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

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