Management of Chronic Noncancer Pain in Depressed Patients
Bibliographic record
Abstract
Many patients with chronic noncancer pain present with comorbid depression, which can greatly complicate the treatment of pain. Chronic pain and depression each increase the risk of licit and illicit substance abuse, including the abuse of opioids, and of suicide. Patients attempting suicide may overdose on opioids, which are commonly perceived as potentially harmful, or acetaminophen, an agent that is believed to be safe but is actually the leading cause of liver failure in the United States. Opioids, acetaminophen, and nonsteroidal anti-inflammatory drugs (NSAIDs) have the potential to interact with antidepressants, and their adverse effects may be exacerbated by alcohol use, which is also common in patients with depression. Topical NSAIDs, capsaicin, and lidocaine provide effective analgesia for several pain conditions. These agents limit systemic drug exposure, reducing the risk of systemic adverse events without risk of accidental or deliberate overdose. However, use of topical agents is generally limited to localized pain syndromes and therefore does not substantially eliminate the need for systemic analgesics in those patients with diffuse persistent pain, central sensitization, and opioid-responsive pain. This review will discuss the challenges associated with treating chronic pain in depressed patients and will provide recommendations for optimizing treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".