Mood- and restraint-based antecedents to binge episodes in bulimia nervosa: possible influences of the serotonin system
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".