Opioids for managing chronic non-malignant pain: safe and effective prescribing.
Bibliographic record
Abstract
OBJECTIVE: To review the evidence on safe and effective prescribing of opioids for chronic non-malignant pain. QUALITY OF EVIDENCE: MEDLINE was searched using the terms "opioid effectiveness" and "adverse effects." There is strong evidence that opioids are effective for both nociceptive and neuropathic pain, but limited evidence that they are effective for pain disorder. There is little information on their effectiveness at high doses or on the adverse effects of high doses. MAIN MESSAGE: Opioids should be initiated after an adequate trial of acetaminophen or nonsteroidal anti-inflammatory drugs for nociceptive pain and of tricyclic antidepressants or anticonvulsants for neuropathic pain. Patients should be asked to sign treatment agreements and to give informed consent to treatment. Patients should experience a graded analgesic response with each dose increase. Titrate doses of immediate-release opioids slowly upward until pain reduction is achieved, and then switch patients to controlled-release opioids. Most patients with chronic non-malignant pain can be managed with<300 mg/d of morphine (or equivalent). CONCLUSION: Opioids are safe and effective for managing chronic pain.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".