The Canadian Network for Mood and Anxiety Treatments (Canmat) Task Force Recommendations for the Management of Patients with Mood Disorders and Comorbid Substance Use Disorders
Bibliographic record
Abstract
Background Mood disorders, especially bipolar disorder (BD), frequently are associated with substance use disorders (SUDs). There are well-designed trials for the treatment of SUDs in the absence of a comorbid condition. However, one cannot generalize these study results to individuals with comorbid mood disorders, because therapeutic efficacy and/or safety and tolerability profiles may differ with the presence of the comorbid disorder. Therefore, a review of the available evidence is needed to provide guidance to clinicians facing the challenges of treating patients with comorbid mood disorders and SUDs. Methods We reviewed the literature published between January 1966 and November 2010 by using the following search strategies on PubMed. Search terms were bipolar disorder or depressive disorder, major (to exclude depression, postpartum; dysthymic disorder; cyclothymic disorder; and seasonal affective disorder) cross-referenced with alcohol or drug or substance and abuse or dependence or disorder . When possible, a level of evidence was determined for each treatment using the framework of previous Canadian Network for Mood and Anxiety Treatments recommendations. The lack of evidence-based literature limited the authors’ ability to generate treatment recommendations that were strictly evidence based, and as such, recommendations were often based on the authors’ opinion. Results Even though a large number of treatments were investigated for alcohol use disorder (AUD), none have been sufficiently studied to justify the attribution of level 1 evidence in comorbid AUD with major depressive disorder (MDD) or BD. The available data allows us to generate first-choice recommendations for AUD comorbid with MDD and only third-choice recommendations for cocaine, heroin, and opiate SUD comorbid with MDD. No recommendations were possible for cannabis, amphet-amines, methamphetamines, or polysubstance SUD comorbid with MDD. First-choice recommendations were possible for alcohol, cannabis, and cocaine SUD comorbid with BD and only second-choice recommendations for heroin, amphetamine, methamphetamine, and polysubstance SUD comorbid with BD. No recommendations were possible for opiate SUD comorbid with BD. Finally, psychotherapies certainly are considered an essential component of the overall treatment of SUDs comorbid with mood disorders. However, further well-designed studies are needed in order to properly assess their potential role in specific SUDs comorbid with a mood disorder. Conclusions Although certain treatments show promise in the management of mood disorders comorbid with SUDs, additional well-designed studies are needed to properly assess their potential role in specific SUDs comorbid with a mood disorder.
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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 | 0.024 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".