Early optimized pharmacological treatment in patients with depression and chronic pain
Bibliographic record
Abstract
Abstract Major depressive disorder (MDD) is the leading cause of disability worldwide. Patients with MDD have high rates of comorbidity with mental and physical conditions, one of which is chronic pain. Chronic pain conditions themselves are also associated with significant disability, and the large number of patients with MDD who have chronic pain drives high levels of disability and compounds healthcare burden. The management of depression in patients who also have chronic pain can be particularly challenging due to underlying mechanisms that are common to both conditions, and because many patients with these conditions are already taking multiple medications. For these reasons, healthcare providers may be reluctant to treat such patients. The Canadian Network for Mood and Anxiety Treatments (CANMAT) guidelines provide evidence-based recommendations for the management of MDD and comorbid psychiatric and medical conditions such as anxiety, substance use disorder, and cardiovascular disease; however, comorbid chronic pain is not addressed. In this article, we provide an overview of the pathophysiological and clinical overlap between depression and chronic pain and review evidence-based pharmacological recommendations in current treatment guidelines for MDD and for chronic pain. Based on clinical experience with MDD patients with comorbid pain, we recommend rapidly and aggressively treating depression according to CANMAT treatment guidelines, using antidepressant medications with analgesic properties, while addressing pain with first-line pharmacotherapy as treatment for depression is optimized. We review options for treating pain symptoms that remain after response to antidepressant treatment is achieved.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".