Clinical Relevance of Food Addiction in Higher Weight Patients across the Binge Eating Spectrum
Bibliographic record
Abstract
Food addiction (FA) is associated with greater severity on many eating-related correlates when comorbid with binge eating disorder (BED) but no study has established this relation across the whole spectrum of binge eating, i.e., from no BED to subthreshold BED to BED diagnosis. This study aims to examine the effect of the presence of FA on the severity of eating behaviors and psychological correlates in patients without BED, subthreshold BED or BED diagnosis. Participants (n = 223) were recruited at a university center specialized in obesity and eating disorder treatment and completed a semi-structured diagnostic interview and questionnaires measuring eating behaviors, emotional regulation, impulsivity, childhood interpersonal trauma, and personality traits. They were categorized by the presence of an eating disorder (no BED, subthreshold BED, or BED) and the presence of FA. Group comparisons showed that, in patients with BED, those with FA demonstrated higher disinhibition (t(79) = −2.19, p = 0.032) and more maladaptive emotional regulation strategies (t(43) = −2.37, p = 0.022) than participants without FA. In patients with subthreshold BED, those with FA demonstrated higher susceptibility to hunger (t(68) = −2.55, p = 0.013) and less cooperativeness (t(68) = 2.60, p = 0.012). In patients without BED, those with FA demonstrated higher disinhibition (t(70) = −3.15, p = 0.002), more maladaptive emotional regulation strategies (t(53) = −2.54, p = 0.014), more interpersonal trauma (t(69) = −2.41, p = 0.019), and less self-directedness (t(70) = 2.14, p = 0.036). We argue that the assessment of FA provides relevant information to complement eating disorder diagnoses. FA identifies a subgroup of patients showing higher severity on many eating-related correlates along the binge eating spectrum. It also allows targeting of patients without a formal eating disorder diagnosis who 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".