Screening and Treating Disordered Eating in Weight Loss Surgery: A Rapid Review of Current Practices and Future Directions
Bibliographic record
Abstract
Disordered eating, such as binge-eating and loss of control eating (LOCE), contribute to suboptimal weight loss and weight regain in some patients who undergo weight loss surgery (WLS). Despite robust evidence linking disordered eating and poor WLS outcomes, there is no consensus on standardized screening and treatment practices for this population. To address this gap, our team conducted a literature review using Ovid MEDLINE, Scopus, CINAHL, EMBASE, and Cochrane CENTRAL, focusing on studies examining screening and treatment of disordered eating in WLS populations. Our review identified key findings related to (a) screening and diagnostic tools, including semi-structured interviews and self-report measures, and (b) psychotherapeutic interventions, including cognitive behavioral therapy (CBT) and other modalities. Findings are inconclusive but suggest avenues for future research examining the routine implementation of post-WLS screening and treatment protocols (including adjunctive pharmacotherapy) for disordered eating.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".