Food addiction to capture severe condition in eating disorder
Bibliographic record
Abstract
Abstract Food addiction (FA) is not in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). However, evidence shows that it increases the severity of eating disorder symptoms, especially when comorbid with binge eating disorder (BED). This study aims to examine the effect of FA on the severity of eating behaviors and psychological correlates in relation with an ED diagnosis. Participants (n = 223) were recruited at the Centre [blind for review] and completed a semi-structured eating disorder diagnosis interview and questionnaires measuring eating behaviors, personality traits, emotional regulation, and childhood interpersonal trauma. They were categorized by the presence of an eating disorder (BED, eating disorder not otherwise specified (EDNOS) or none) and the presence of FA. Group comparisons showed that, in patients with BED, those with FA demonstrated higher disinhibition and more maladaptive emotional regulation strategies than participants without FA. In patients without an eating disorder diagnosis, those with FA demonstrated higher disinhibition, more maladaptive emotional regulation strategies, more interpersonal trauma, and less self-directedness. The assessment of FA combined with the diagnostic assessment of eating disorder provides a better understanding of the severity of the pathology. First, in presence of BED, FA allows to target a subgroup of patients showing higher severity. Second, FA allows to target patients without an eating disorder diagnosis that would still benefit from professional help.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".