Disgust sensitivity and behavioural inhibitory systems in binge eating disorder: associations with eating pathology
Bibliographic record
Abstract
Disgust sensitivity refers to how unpleasant a disgusting experience is to an individual and is involved in the development of many psychiatric conditions. Given its link with food ingestion, there is an interest in understanding how an individual's susceptibility to disgust relates to dietary habits. One possible mechanism giving rise to this association is through the effects negative emotions have on high-order cognitive processes, but few studies take this model into account. The aim of this study was to characterize general disgust sensitivity in a clinical binge eating disorder (BED) population, and explore whether disgust sensitivity relates to inhibitory control and eating pathology. Following a case-controlled study design, our results show that: (1) disgust sensitivity and its subscales do not differ between BED and healthy controls, (2) higher disgust sensitivity in BED relates to greater behavioural inhibition, (3) inhibitory control reaction times relate to aspects of eating pathology, and (4) inhibitory control does not mediate relationships between disgust sensitivity and BMI among participants with BED. Understanding the role of disgust sensitivity in BED may allow us to understand how negative emotion systems maintain dysregulated eating behaviours with the potential to inform emotion-regulation treatment approaches. Level of evidence: Level III: Evidence obtained from well-designed cohort or case-control analytic studies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".