The Canadian Network for Mood and Anxiety Treatments (Canmat) Task Force Recommendations for the Management of Patients with Mood Disorders and Select Comorbid Medical Conditions
Bibliographic record
Abstract
Background Medical comorbidity in patients with mood disorders has become an increasingly important clinical and global public health issue. Several specific medical conditions are associated with an increased risk of mood disorders, and conversely, mood disorders are associated with increased morbidity and mortality in patients with specific medical disorders. Methods To help understand the bidirectional relationship and to provide an evidence-based framework to guide the treatment of mood disorders that are comorbid with medical illness, we have reviewed relevant articles and reviews published in English-language databases (to April 2011) on the links between mood disorders and several common medical conditions, evaluating the efficacy and safety of pharmacologic and psychosocial treatments. The medical disorders most commonly encountered in adult populations (ie, cardiovascular disease, cerebrovascular disease, cancer, human immunodeficiency virus, hepatitis C virus, migraine, multiple sclerosis, epilepsy, and osteoporosis) were chosen as the focus of this review. Results Emerging evidence suggests that depression comorbid with several medical disorders is treatable and failure to treat depression in medically ill patients may have a negative effect on medical outcomes. Conclusions This review summarizes the available evidence and provides treatment recommendations for the management of comorbid depression in medically ill patients.
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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 | 0.001 | 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".