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Record W2054905401 · doi:10.1017/s0033291705005817

Mood- and restraint-based antecedents to binge episodes in bulimia nervosa: possible influences of the serotonin system

2005· article· en· W2054905401 on OpenAlexaff
Howard Steiger, Lise Gauvin, Marla Engelberg, N.M.K. Ng Ying Kin, Mimi Israël, Stephen A. Wonderlich, Jodie Richardson

Bibliographic record

VenuePsychological Medicine · 2005
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteDouglas College
Fundersnot available
KeywordsMoodBulimia nervosaParoxetinePsychologyBinge eatingDietingSerotoninClinical psychologyPsychiatryEating disordersDevelopmental psychologyEndocrinologyInternal medicineMedicineObesityAntidepressantWeight lossAnxiety

Abstract

fetched live from OpenAlex

BACKGROUND: In bulimic syndromes, binge episodes are thought to be caused by dietary restraint and negative moods. However, as central serotonin (5-hydroxytryptamine: 5-HT) mechanisms regulate appetite and mood, the 5-HT system could be implicated in diet- and mood-based binge antecedents. METHOD: We used hand-held computers to obtain repeated "online" measurements of eating behaviors, moods, and self-concepts in 21 women with bulimic syndromes, and modeled 5-HT system activity with a measure of platelet [3H]paroxetine-binding density. RESULTS: Mood and self-concept ratings were found to be worse before binge episodes (than at other moments), and cognitive restraint was increased. After binges, mood and self-concept deteriorated further, and thoughts of dieting became more intense. Intriguingly, lower paroxetine-binding density predicted poorer mood and self-concept before a binge, larger post-binge decrements in mood and self-concept, and larger post-binge increases in dietary restraint. CONCLUSIONS: Paroxetine binding thus seemed to reflect processes that impacted upon mood-related antecedents to binge episodes, and consequences implicating mood and dietary restraint.

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 score0.040
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.044
GPT teacher head0.372
Teacher spread0.327 · 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

Citations51
Published2005
Admission routes1
Has abstractyes

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