Canadian Guideline for Safe and Effective Use of Opioids for Chronic Non‐Cancer Pain: Implications for Pain Physicians
Bibliographic record
Abstract
C hronic pain is a serious public health concern, causing widespread suffering and disability, and increased health care costs (1).Potent opioids have become a mainstay of chronic pain treatment, and both the number of patients taking opioids and the mean dose per patient have increased substantially in North America.Unfortunately, this has been accompanied by marked increases in opioid-related deaths, diversion, misuse and addiction, generating concern among the public and the medical profession.For a guideline to be credible, it should be based on a comprehensive search of controlled trials and other studies, supplemented by the perspectives of experienced clinicians and opinion leaders.It should address questions important to public health and provide practical guidance to clinicians.These goals have been achieved in the recently published "Canadian Guideline for Safe and Effective Use of Opioids for Chronic Non-Cancer Pain" (2).Under the umbrella of the Federation of Medical Regulatory Authorities of Canada, the National Opioid Use Guideline Group was created in 2008 to collaborate on guideline development and implementation.A core research group was formed, which conducted a systematic review of the effectiveness of opioids for chronic noncancer pain (CNCP), as well as focused reviews on topics such as medical complications of opioids and use of opioids in specific populations.The core group wrote an initial draft of the guideline.A 49-member panel that included physicians from family medicine, physiatry, neurology and addiction medicine, and various other health care providers, reviewed the draft and provided feedback through a structured approach.All major regions of Canada were represented on the panel, which included members of national organizations such as the Canadian Pain Society.The panel achieved consensus on 24 recommendations.Some highlights of the guideline are provided below.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.009 | 0.002 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".