Cannabis and Cannabinoids in Adults With Cancer: ASCO Guideline
Bibliographic record
Abstract
PURPOSE: To guide clinicians, adults with cancer, caregivers, researchers, and oncology institutions on the medical use of cannabis and cannabinoids, including synthetic cannabinoids and herbal cannabis derivatives; single, purified cannabinoids; combinations of cannabis ingredients; and full-spectrum cannabis. METHODS: A systematic literature review identified systematic reviews, randomized controlled trials (RCTs), and cohort studies on the efficacy and safety of cannabis and cannabinoids when used by adults with cancer. Outcomes of interest included antineoplastic effects, cancer treatment toxicity, symptoms, and quality of life. PubMed and the Cochrane Library were searched from database inception to January 27, 2023. ASCO convened an Expert Panel to review the evidence and formulate recommendations. RESULTS: The evidence base consisted of 13 systematic reviews and five additional primary studies (four RCTs and one cohort study). The certainty of evidence for most outcomes was low or very low. RECOMMENDATIONS: Cannabis and/or cannabinoid access and use by adults with cancer has outpaced the science supporting their clinical use. This guideline provides strategies for open, nonjudgmental communication between clinicians and adults with cancer about the use of cannabis and/or cannabinoids. Clinicians should recommend against using cannabis or cannabinoids as a cancer-directed treatment unless within the context of a clinical trial. Cannabis and/or cannabinoids may improve refractory, chemotherapy-induced nausea and vomiting when added to guideline-concordant antiemetic regimens. Whether cannabis and/or cannabinoids can improve other supportive care outcomes remains uncertain. This guideline also highlights the critical need for more cannabis and/or cannabinoid research.Additional information is available at www.asco.org/supportive-care-guidelines.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".