Food Addiction Is Associated with Binge Eating and Psychiatric Distress among Post-Operative Bariatric Surgery Patients and May Improve in Response to Cognitive Behavioural Therapy
Bibliographic record
Abstract
The current study examined clinical correlates of food addiction among post-operative bariatric surgery patients, compared the clinical characteristics of patients with versus without food addiction, and examined whether a brief telephone-based cognitive behavioural therapy (Tele-CBT) intervention improves food addiction symptomatology among those with food addiction. Participants (N = 100) completed measures of food addiction, binge eating, depression, and anxiety 1 year following bariatric surgery, were randomized to receive either Tele-CBT or standard bariatric post-operative care, and then, repeated the measure of food addiction at 1.25 and 1.5 years following surgery. Thirteen percent of patients exceeded the cut-off for food addiction at 1 year post-surgery, and this subgroup of patients reported greater binge eating characteristics and psychiatric distress compared to patients without food addiction. Among those with food addiction, Tele-CBT was found to improve food addiction symptomatology immediately following the intervention. These preliminary findings suggest that Tele-CBT may be helpful, at least in the short term, in improving food addiction symptomatology among some patients who do not experience remission of food addiction following bariatric surgery; however, these findings require replication in a larger sample.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".