Assessment and Monitoring of Patients With Chronic Pain and Co-occurring Substance Use and Abuse
Bibliographic record
Abstract
Substance abuse complicates pain management. The comorbidity of substance abuse and pain is particularly problematic in the United States and Canada, substantially more than in most countries with advanced health care systems. Treatment of pain with long-term opioids, particularly in high doses, is known to be associated with substantial medical comorbidity, unintentional overdoses, and death. Treatment of opioid dependence in the chronic pain patient is necessary for effective pain management, whether or not the patient uses drugs illicitly. Opioids, particularly in high doses, produce central nervous system neuroadaptations that reduce or eliminate analgesic effectiveness and enhance sensitivity to pain in general. The neuroadaptations often result in opioid dependency and, in the long-term, craving. Weaning patients from chronic opioids can be exquisitely difficult if simple dose reduction is attempted. The process can be quite successful and gratifying, however, if certain principles are followed. These include education, comfortable detoxification using long-acting opioids, usually methadone or buprenorphine, nonopioid pain management, psychological support, and coordinated care.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".