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Record W3094022501 · doi:10.1080/10640266.2020.1836907

Psychological characteristics and childhood adversity of adolescents with atypical anorexia nervosa versus anorexia nervosa

2020· article· en· W3094022501 on OpenAlexaff
Ashley Pauls, Gina Dimitropoulos, Gisele Marcoux‐Louie, Manya Singh, Scott B. Patten

Bibliographic record

VenueEating Disorders · 2020
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsAlberta Children's HospitalAlberta Health ServicesAlberta HealthUniversity of Calgary
Fundersnot available
KeywordsAnorexia nervosaPsychosocialPsychologyClinical psychologyDistressPsychiatryQuality of life (healthcare)Eating disordersPsychotherapist

Abstract

fetched live from OpenAlex

The assessment and diagnosis of atypical anorexia nervosa (AAN) is an ongoing challenge for clinicians. This study aims to examine psychological morbidity and exposure to childhood adversity in adolescents with AAN compared to adolescents with anorexia nervosa, restricting type (AN-R). This registry-based study compared 42 adolescents with AAN to 79 adolescents with AN-R on a variety of psychosocial measures at the time of presentation to a specialized eating disorder program. In contrast to AN-R, adolescents with AAN had more severe drive for thinness (p =.011), body dissatisfaction (p =.038), and lower quality of life (p =.047), but had better global functioning (p =.032). Adolescents who had high Adverse Childhood Experiences (ACE) Questionnaire scores (ACE score ≥ 4) had over 5 times higher odds of having AAN than those who did not have high ACE scores (p =.008). There was no significant difference between groups on measures of low self-esteem and non-accidental self-injury. Adolescents with AAN presented with similar or more severe psychosocial distress compared to their peers with AN-R across a majority of the measures. The findings highlight the need to address trauma, body-related difficulties, and quality of life in the assessment and treatment of adolescents with AAN.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

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 head0.021
GPT teacher head0.281
Teacher spread0.260 · 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 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

Citations36
Published2020
Admission routes1
Has abstractyes

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