Cannabis and cannabinoids in cancer pain management
Bibliographic record
Abstract
PURPOSE OF REVIEW: An increasing number of patients are turning to cannabis and cannabinoids for management of their palliative and nonpalliative cancer pain and other cancer-related symptoms. Canadians have a legal framework for access to medical cannabis, which provides a unique perspective in a setting lacking robust clinical evidence. This review seeks to delineate the role of cannabis and cannabinoids in cancer pain management and offers insight into the Canadian practice. RECENT FINDINGS: A cohort study using nabiximols on advanced cancer pain in patients already optimized on opioids, over 3 weeks, demonstrated improved average pain score. A large observational study of cancer patients using cannabis over 6 months demonstrated a decreased number of patients with severe pain and decreased opioid use, whereas the number of patients reporting good quality of life increased. SUMMARY: Good preclinical animal data and a large body of observational evidence point to the potential efficacy of cannabinoids for cancer pain management. However, there are relatively weak data pointing to clinical efficacy from clinical trial data to date. In Canada, the burgeoning cannabis industry has driven the population to embrace a medicine before clinical evidence. There remains a need for high-quality randomized controlled trials to properly assess the effectiveness and safety of medical cannabis, compared with placebo and standard treatments for cancer-related symptoms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".