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Record W2753999685 · doi:10.1002/eat.22776

Symptom trajectories throughout two family therapy treatments for adolescent anorexia nervosa

2017· article· en· W2753999685 on OpenAlexaff
Stuart B. Murray, Eva Pila, Daniel Le Grange, Susan M. Sawyer, Elizabeth K. Hughes

Bibliographic record

VenueInternational Journal of Eating Disorders · 2017
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsYork UniversityUniversity of Toronto
Fundersnot available
KeywordsAnorexia nervosaAffect (linguistics)PsychologyWeight gainRandomized controlled trialAnorexiaFamily therapyEating disordersMedicineInternal medicineClinical psychologyPsychiatryBody weight

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aimed to examine the trajectory of symptom remission and affective functioning throughout the course of two family-based treatments for adolescent anorexia nervosa (AN): conjoint family-based treatment (FBT) and parent-focused treatment (PFT). METHOD: = 15.5 years, SD = 1.5) with a primary diagnosis of AN who participated in a randomized clinical trial comparing FBT (N = 55) and PFT (N = 51). Patient weight and self-reported assessments of dietary restraint and positive and negative affect were recorded at regular intervals throughout treatment. RESULTS: Multilevel models revealed increases in weight (β = 0.33, p < .001) and positive affect (β = 0.03, p < .001), and decreases in dietary restraint (β = -0.03, p < .001) and negative affect (β = -0.04, p < .001) over the course of treatment. No significant effects emerged by treatment type. DISCUSSION: These findings suggest that PFT may bring about comparable trajectories of weight gain and reduced dietary restraint as conjoint FBT, despite adolescents not being directly involved in treatment. These findings also highlight that the exclusively behavioral focus throughout both PFT and FBT is associated with significant increments in positive affect and significant reductions in negative affect.

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 score0.111
Threshold uncertainty score0.777

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 science0.0010.000
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 head0.047
GPT teacher head0.407
Teacher spread0.360 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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