Recognizing Binge-Eating Disorder in the Clinical Setting
Bibliographic record
Abstract
Article AbstractObjective: Review the clinical skills needed to recognize, diagnose, and manage binge-eating disorder (BED) in a primary care setting.Data Sources: A PubMed search of English-language publications (January 1, 2008-December 11, 2014) was conducted using the term binge-eating disorder. Relevant articles known to the authors were also included.Study Selection/Data Extraction: Publications focusing on preclinical topics (eg, characterization of receptors and neurotransmitter systems) without discussing clinical relevance were excluded. A total of 101 publications were included in this review.Results: Although BED is the most prevalent eating disorder, it is underdiagnosed and undertreated. BED can be associated with medical (eg, type 2 diabetes and metabolic syndrome) and psychiatric (eg, depression and anxiety) comorbidities that, if left untreated, can impair quality of life and functionality. Primary care physicians may find diagnosing and treating BED challenging because of insufficient knowledge of its new diagnostic criteria and available treatment options. Furthermore, individuals with BED may be reluctant to seek treatment because of shame, embarrassment, and a lack of awareness of the disorder. Several short assessment tools are available to screen for BED in primary care settings. Pharmacotherapy and psychotherapy should focus on reducing binge-eating behavior, thereby reducing medical and psychiatric complications.Conclusions: Overcoming primary care physician- and patient-related barriers is critical to accurately diagnose and appropriately treat BED. Primary care physicians should take an active role in the initial recognition and assessment of suspected BED based on case-finding indicators (eg, eating habits and being overweight), the initial treatment selection, and the long-term follow-up of patients who meet DSM-5 BED diagnostic criteria.†‹
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".