Binge eating disorder
Bibliographic record
Abstract
Objective To provide an updated overview of binge eating disorder (BED) that includes recommendations relevant for primary care practitioners. Quality of evidence PubMed, Google Scholar, and PsycInfo were searched with no time restriction using the subject headings binge eating disorder, treatment, review, guidelines, psychotherapy, primary care, and pharmacotherapy. Levels of evidence for all treatment recommendations ranged from I to III. Main message Binge eating disorder is associated with considerable patient distress and impairment, as well as medical and psychiatric comorbidities, and was added to the Diagnostic and Statistical Manual of Mental Disorders, 5th edition, in 2013. Primary care practitioners are well suited to screen, diagnose, and initiate treatment for BED. A stepped-care approach to treatment starts with guided self-help, adding or moving to pharmacotherapy or individual psychotherapy as needed. The psychotherapies with the most research support include cognitive behaviour therapy, interpersonal therapy, and dialectical behaviour therapy. In terms of pharmacotherapy, evidence supports the use of lisdexamfetamine, antidepressant medications, and anticonvulsant medications. Conclusion This overview provides guidance on screening, diagnosis, and treatment approaches based on the currently available evidence, as well as expert opinions of a diverse group of experts to help guide clinicians where evidence is limited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".