A Scoping Review on Clinical Trials of Pain Reduction With Cannabis Administration in Adults
Bibliographic record
Abstract
Indications of cannabis use are numerous although the indication to relief pain remains a major research interest and clinical application. Studies investigating the effect of herbal cannabis and cannabis-based medicine on neuropathic, non-neuropathic pain, acute pain and experimentally induced pain were reviewed. A search was performed in PubMed and Cochrane library for articles published in English between January 1, 2000 and May 8, 2020. The search terms used were related to cannabis and pain in adults. We identified 34 studies, of which 30 were randomized controlled clinical trials (RCTs). Varying effects were identified from the RCTs, and as expected more promising effects from non-RCTs. Cannabis-based medications were found most effective as an adjuvant therapy in refractory multiple sclerosis, and weak evidence was found to support the treatment of cancer pain especially in advanced stages. Chronic rheumatic pain showed promising results. Adverse events of cannabis-based treatment were found to be more frequent with tetrahydrocannabinol herbal strains compared to other cannabis-derived products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".