Comorbid Diabetes and Eating Disorders in Adult Patients
Bibliographic record
Abstract
PURPOSE: The lack of research concerning treatment for individuals with diabetes mellitus (DM) and comorbid eating disorders (ED) contributes to the gulf between the psychosocial needs of individuals with the two conditions and the treatment they receive. Empirical evidence has established that the prognosis of patients with this comorbid diagnosis (ED-DM) is poor in the absence of a specialized DM treatment specifically adapted to ED. In individuals with DM, comorbid ED is associated with numerous complications. Despite these interactions, current knowledge about the comorbid diagnosis is limited, and eating disorders in patients with diabetes often remain undiagnosed. This article presents standard procedures for assessment and optimal therapeutic interventions for patients with ED and DM. CONCLUSION: In patients with diabetes, problematic eating behaviors and symptoms should be assessed routinely. When an eating disorder is detected, diabetes management needs to be adapted, binge eating or medication misuse needs to be addressed, and eating disorder specialists should be included in the multidisciplinary team.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".