Therapeutic Use of Cannabis and Cannabinoids
Bibliographic record
Abstract
Importance: Approximately 27% of adults in the US and Canada report having ever used cannabis for medical purposes. An estimated 10.5% of the US population reports using cannabidiol (CBD), a chemical compound extracted from cannabis that does not have psychoactive effects, for therapeutic purposes. Observations: Conditions for which cannabinoids have approval from the US Food and Drug Administration include HIV/AIDS-related anorexia, chemotherapy-induced nausea and vomiting, and certain pediatric seizure disorders. A meta-analysis of randomized clinical trials reported a small but significant reduction in nausea and vomiting from various causes (eg, chemotherapy, cancer) when comparing prescribed cannabinoids (eg, dronabinol, nabilone) with placebo or active comparators (eg, alizapride, chlorpromazine; standardized mean difference [SMD], -0.29 [95% CI, -0.39 to -0.18]). A meta-analysis of randomized clinical trials among patients with HIV/AIDS reported that cannabinoids had a moderate effect on increasing body weight compared with placebo (SMD, 0.57 [95% CI, 0.22 to 0.92]). Evidence-based guidelines do not recommend the use of inhaled or high-potency cannabis (≥10% or 10 mg Δ9-tetrahydrocannabinol [Δ9-THC]) for medical purposes. High-potency cannabis compared with low-potency cannabis use is associated with increased risk of psychotic symptoms (12.4% vs 7.1%) and generalized anxiety disorder (19.1% vs 11.6%). A meta-analysis of observational studies reported that 29% of individuals who used cannabis for medical purposes met criteria for cannabis use disorder. Daily inhaled cannabis use compared with nondaily use was associated with an increased risk of coronary heart disease (2.0% vs 0.9%), myocardial infarction (1.7% vs 1.3%), and stroke (2.6% vs 1.0%). Evidence from randomized clinical trials does not support the use of cannabis or cannabinoids for most conditions for which it is promoted, such as acute pain and insomnia. Before considering cannabis or cannabinoids for medical use, clinicians should consult applicable institutional, state, and national regulations; evaluate for drug-drug interactions; and assess for contraindications (eg, pregnancy) or conditions in which risks likely outweigh benefits (eg, schizophrenia or ischemic heart disease). For patients using cannabis or cannabinoids for treatment of medical conditions, clinicians should discuss harm reduction strategies, including avoiding concurrent use with alcohol or other central nervous system depressants such as benzodiazepines, using the lowest effective dose, and avoiding use when driving or operating machinery. Conclusions and Relevance: Evidence is insufficient for the use of cannabis or cannabinoids for most medical indications. Clear guidance from clinicians is essential to support safe, evidence-based decision-making. Clinicians should weigh benefits against risks when engaging patients in informed discussions about cannabis or cannabinoid use.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".