Low quality of life in binge eating disorder compared to healthy controls.
Bibliographic record
Abstract
INTRODUCTION: Patients with binge eating disorder (BED) tend to report a lower quality of life (QoL) than patients with other eating disorders. However, most research on QoL in eating disorders include generic rather than disease-specific measures. Depression and obesity are frequent comorbid conditions in patients with BED affecting QoL. In the present study, we aimed to assess disease-specific QoL in BED and to investigate the impact of obesity and depression. METHODS: Adult patients who met the DSM-5 criteria for BED (N = 98) were recruited from a newly established specialised online treatment programme for BED and completed the following questionnaires: the Eating Disorder Quality of Life Questionnaire (EDQLS), the Major Depression Inventory (MDI) and the newly introduced Binge Eating Disorder Questionnaire for measuring BED severity. Healthy, normal-weight individuals were recruited through online invitations on social media, n = 190. RESULTS: QoL in BED individuals was significantly lower than in healthy individuals. No relationship was found between BMI and EDQLS, whereas significant, negative correlations were found between depression and all subscales of the EDQLS. CONCLUSION: Disease-specific QoL in BED was associated with depression but not with BMI. FUNDING: none. CLINICALTRIALS: gov NCT05010798.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 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".