Cannabis or Cannabinoids for the Management of Chronic Noncancer Pain: Best Practice Advice From the American College of Physicians
Bibliographic record
Abstract
DESCRIPTION: The American College of Physicians' Population Health and Medical Science Committee (PHMSC) developed this best practice advice to inform clinicians about what is currently known about the benefits and harms of cannabis or cannabinoids in the management of chronic noncancer pain and to provide advice for clinicians counseling patients seeking this therapy. METHODS: The PHMSC considers areas where evidence is uncertain or emerging or practice does not follow the evidence to provide clinical advice based on a review and assessment of scientific work, including systematic reviews and individual studies. Sources of evidence included a living systematic review on cannabis and cannabinoid treatments for chronic noncancer pain and a series of living systematic reviews and primary studies. BEST PRACTICE ADVICE 1A: Clinicians should counsel patients about the benefits and harms of cannabis or cannabinoids when patients are considering whether to start or continue to use cannabis or cannabinoids to manage their chronic noncancer pain. BEST PRACTICE ADVICE 1B: Clinicians should counsel the following subgroups of patients that the harms of cannabis or cannabinoid use for chronic noncancer pain are likely to outweigh the benefits: young adult and adolescent patients, patients with current or past substance use disorder, patients with serious mental illness, and frail patients and those at risk for falling. BEST PRACTICE ADVICE 2: Clinicians should advise against starting or continuing to use cannabis or cannabinoids to manage chronic noncancer pain in patients who are pregnant or breastfeeding or actively trying to conceive. BEST PRACTICE ADVICE 3: cannabis to manage chronic noncancer 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 | 0.012 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".