The Canadian Network for Mood and Anxiety Treatments (Canmat) Task Force Recommendations for the Management of Patients with Mood Disorders and Comorbid Metabolic Disorders
Bibliographic record
Abstract
Background One goal of the Canadian Network for Mood and Anxiety Treatments (CANMAT) is to develop evidence-based and best practice educational programs and recommendations. Our group conducted a comprehensive literature review to provide evidence-based recommendations for treating metabolic comorbidity in individuals with major depressive disorder (MDD) and bipolar disorder (BD). Methods We searched PubMed for all English-language articles published January 1966 to November 2010 using BD and MDD cross-referenced with metabolic syndrome , obesity , diabetes mellitus , hypertension , and dyslipidemia . That search was augmented by a review of articles reporting outcomes of an intervention targeting components of metabolic syndrome in individuals with MDD or BD. Results Consensus exists for the recommendation that individuals with MDD and BD should be routinely screened for risk factors that increase risk for metabolic syndrome. For excess weight, the best-studied pharmacologic approaches are metformin and topiramate, with emerging evidence for liraglutide and modafinil. For binge eating disorder, the best evidence in mood disorders was for cognitive-behavioral therapy as well as topiramate, zonisamide, and in select cases selective serotonin reuptake inhibitors. For dysglycemia, dyslipidemia, and hypertension, evidence supports cognitive-behavioral interventions and anti-diabetic, antilipidemic, and antihypertensive treatments. Conclusions Comprehensive care of individuals with mood disorders should include routine evaluation of the risk and presence of metabolic syndrome and its components. Systematic evaluation of preventative and targeted treatments of metabolic syndrome in mood disorder populations is insufficient.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".