A retrospective chart review study of symptom onset, diagnosis, comorbidities, and treatment in patients with binge eating disorder in Canadian clinical practice
Bibliographic record
Abstract
PURPOSE: In the Canadian healthcare setting, there is limited understanding of the pathways to diagnosis and treatment for patients with binge eating disorder (BED). METHODS: This retrospective chart review examined the clinical characteristics, diagnostic pathways, and treatment history of adult patients diagnosed with BED. RESULTS: Overall, 202 charts from 57 healthcare providers (HCPs) were reviewed. Most patients were women (69%) and white (78%). Mean ± SD patient age was 37 ± 12.1 years. Comorbidities identified in > 20% of patients included obesity (50%), anxiety (49%), depression and/or major depressive disorder (46%), and dyslipidemia (26%). Discussions regarding a diagnosis of BED were typically initiated more often by HCPs than patients. Most patients (64%) received a diagnosis of BED ≥ 3 years after symptom onset. A numerically greater percentage of patients received (past or current) nonpharmacotherapy than pharmacotherapy (84% vs. 67%). The mean ± SD number of binge eating episodes/week numerically decreased from pretreatment to follow-up with lisdexamfetamine (5.4 ± 2.8 vs. 1.7 ± 1.2), off-label pharmacotherapy (4.7 ± 3.9 vs. 2.0 ± 1.13), and nonpharmacotherapy (6.3 ± 4.8 vs. 3.5 ± 6.0) Across pharmacotherapies and nonpharmacotherapies, most patients reported improvement in symptoms of BED (84-97%) and in overall well-being (80-96%). CONCLUSIONS: These findings highlight the importance of timely diagnosis and treatment of BED. Although HCPs are initiating discussions about BED, earlier identification of BED symptoms is required. Furthermore, these data indicate that pharmacologic and nonpharmacologic treatment for BED is associated with decreased binge eating and improvements in overall well-being. LEVEL OF EVIDENCE: IV, chart review.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".