Integrated treatment for comorbid eating disorders and substance use disorders: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This review will identify and summarize the literature on the integrated treatment of comorbid eating disorders and substance use disorders, focusing on clinical practice guidelines and treatment studies. INTRODUCTION: Eating disorders and substance use disorders are the deadliest psychiatric conditions, frequently co-occur, and are linked to greater symptom severity and poorer treatment outcomes. Despite repeated calls for their integrated treatment, such an approach has rarely been empirically evaluated. To advance the development of integrated treatments for comorbid eating disorders and substance use disorders, a critical first step is to describe existing treatment guidelines and summarize research evidence for this approach. INCLUSION CRITERIA: This review will consider all peer-reviewed and gray literature describing the integrated treatment of comorbid eating disorders and substance use disorders, focusing on i) clinical practice guidelines; and ii) treatment studies. We will not place limitations on populations, types of eating disorders, types of substance use disorders, or other contextual factors. METHODS: Databases to be searched will include MEDLINE (Ovid), Embase, CINAHL (EBSCOhost), PsycINFO (EBSCOhost), Scopus, and clinical practice guidelines databases identified by CADTH Grey Matters. No date or language limits will be applied to the search. At the screening stage, we will only consider literature in English or French. Two independent reviewers will screen studies at the title/abstract and full-text levels, and extract relevant studies. Disagreements will be resolved through discussion. Findings will be presented in tabular format and a narrative summary. REVIEW REGISTRATION: Open Science Framework https://osf.io/za35j/.
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 | 0.084 | 0.072 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.066 | 0.015 |
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".