A selective review of medical cannabis in cancer pain management
Bibliographic record
Abstract
Insufficient management of cancer-associated chronic and neuropathic pain adversely affects patient quality of life. Patients who do not respond well to opioid analgesics, or have severe side effects from the use of traditional analgesics are in need of alternative therapeutic op-tions. Anecdotal evidence suggests that medical cannabis has potential to effectively manage pain in this patient population. This review presents a selection of representative clinical studies, from small pilot studies conducted in 1975, to double-blind placebo-controlled trials conducted in 2014 that evaluated the efficacy of cannabinoid-based therapies containing tetrahydrocannabinol (THC) and cannabidiol (CBD) for reducing cancer-associated pain. A review of literature published on Medline between 1975 and 2017 identified five clinical studies that evaluated the effect of THC or CBD on controlling cancer pain, which have been reviewed and summarised. Five studies that evaluated THC oil capsules, THC:CBD oromucosal spray (nabiximols), or THC oromucosal sprays found some evidence of cancer pain reduction associated with these therapies. A variety of doses ranging from 2.7-43.2 mg/day THC and 0-40 mg/day CBD were administered. Higher doses of THC were correlated with increased pain relief in some studies. One study found that significant pain relief was achieved in doses as low as 2.7-10.8 mg THC in combination with 2.5-10.0 mg CBD, but there was conflicting evidence on whether higher doses provide superior pain relief. Some reported side effects include drowsiness, hypotension, mental clouding, and nausea and vomiting. There is evidence suggesting that medical cannabis reduces chronic or neu-ropathic pain in advanced cancer patients. However, the results of many studies lacked statistical power, in some cases due to limited number of study subjects. Therefore, there is a need for the conduct of further double-blind, placebo-controlled clinical trials with large sample sizes in order to establish the optimal dosage and efficacy of different cannabis-based therapies.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".