Outcomes for binge eating disorder in a remote weight-inclusive treatment program: a case report
Bibliographic record
Abstract
BACKGROUND: There are no known published reports on outcomes for medically and psychiatrically compromised patients with binge eating disorder (BED) treated remotely in higher level of care settings. This case report presents outcomes of an intentionally remote weight-inclusive partial hospitalization and intensive outpatient program based on Health at Every Size® and intuitive eating principles. CASE PRESENTATION: The patient presented with an extensive trauma background and long history of disturbed eating and body image. She was diagnosed with BED along with several comorbidities, most notably major depressive disorder with suicidality and non-insulin dependent diabetes mellitus. She completed a total of 186 days in the comprehensive, multidisciplinary treatment program encompassing individual and group therapy, as well as other supportive services such as meal support and in vivo exposure sessions. Upon discharge, her BED was in remission, her major depressive disorder was in partial remission, and she no longer exhibited signs of suicidality. Overall, she showed decreases in eating disorder, depressive, and anxiety symptoms as well as increases in quality of life and intuitive eating throughout treatment, which were largely maintained after one year. CONCLUSIONS: This case highlights the potential of remote treatment as an option for individuals with BED, especially in cases where access to higher levels of care might be limited. These findings exemplify how a weight-inclusive approach can be effectively applied when working with this population.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".