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Record W2096126765 · doi:10.1176/ajp.2007.164.6.970

Does Percent Body Fat Predict Outcome in Anorexia Nervosa?

2007· article· en· W2096126765 on OpenAlexfundno aff
Laurel Mayer, Christina A. Roberto, Deborah R. Glasofer, Sarah Fischer Etu, Dympna Gallagher, Jack Wang, Steven B. Heymsfield, Richard N. Pierson, Evelyn Attia, Michael J. Devlin, B. Timothy Walsh

Bibliographic record

VenueAmerican Journal of Psychiatry · 2007
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesUniversity of TorontoGlaxoSmithKlinePfizerEli Lilly and CompanyNational Institute of Mental HealthAbbott Laboratories
KeywordsAnorexia nervosaBody mass indexAnorexiaInternal medicineFluoxetineEndocrinologyMedicinePsychologyLeptinEating disordersObesityPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: The goal of this study was to investigate the relationship of body composition and neuroendocrine levels with clinical outcome in women with anorexia nervosa in a relapse-prevention trial. METHOD: Body composition and fasting cortisol and leptin levels were assessed before random assignment in 32 weight-recovered subjects with anorexia nervosa from the New York site of the Fluoxetine to Prevent Relapse in Women With Anorexia Nervosa trial. Clinical outcome at the end of study participation was defined using modified Morgan-Russell criteria (full, good, fair, poor), then dichotomized into treatment "success" or "failure." RESULTS: In a binary logistic regression model examining the effect of percent body fat, body mass index, anorexia nervosa subtype, waist-to-hip ratio, and serum cortisol and leptin levels on treatment outcome, only percent body fat was significantly associated with outcome. CONCLUSIONS: In recently weight-restored women with anorexia nervosa, lower percent body fat was associated with poor long-term outcome.

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)0.001
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 score0.019
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.323
Teacher spread0.313 · 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

Citations53
Published2007
Admission routes1
Has abstractyes

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