The opioid epidemic and national guidelines for opioid therapy for chronic noncancer pain: a perspective from different continents
Bibliographic record
Abstract
INTRODUCTION: A marked rise in opioid prescriptions for patients with chronic noncancer pain (CNCP) with a parallel increase in opioid abuse/misuse, and resulting deaths was noted in the Unites states in the past decade (opioid epidemic). In response, the US Center of Diseases Control (CDC) developed a guideline for prescribing of opioids for patients with CNCP. OBJECTIVES: To assess (1) if there is an opioid epidemic in Australia, Canada, and Germany (2) to compare Australian, Canadian, German, and Center of Diseases Control guidelines recommendations for long-term opioid therapy for CNCP. METHODS: National evidence-based guidelines and PubMed were searched for recommendations for opioid prescriptions for CNCP. RESULTS: There are signs of an opioid epidemic in Australia and Canada, but not in Germany. Guidelines in all 4 countries provide similar recommendations: opioids are not the first-line therapy for patients with CNCP; regular clinical assessments of benefits and harms are necessary; excessive doses should be avoided (recommended morphine equivalent daily doses range from 50 to 200 mg/d); stopping rules should be followed. All guidelines do not recommend the use of opioids in chronic pain conditions without an established nociceptive or neuropathic cause such as fibromyalgia and primary headache. CONCLUSION: Implementation of opioid prescribing guidelines should ensure that physicians prescribe opioids only for appropriate indications in limited doses for selected patients and advice patients on their safe use. These measures could contribute to reduce prescription opioid misuse/abuse and deaths.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".