TITLE: Cannabinoids as Co-Analgesics: Review of Clinical Effectiveness
Bibliographic record
Abstract
For centuries cannabis has been used for pain relief. 1,2 Cannabinoids are the compounds of cannabis that produce therapeutic as well as psychotropic effects. 3 Available cannabinoid drugs include delta nine-tetrahydrocannabinol (Δ-9-THC), dronabinol (a synthetic Δ-9-THC), nabilone, and a combination of synthetic Δ-9-THC and cannabidiol (THC/CBD; Sativex). 4 Sativex was approved by Health Canada in 2005 for use as an adjunctive treatment for neuropathic pain for multiple sclerosis patients. 4,5 Nabilone (Cesamet) is approved for treatment and management of severe nausea and vomiting induced by chemotherapy, 4 but there is clinical evidence supporting its use for pain management. 6,7 Studies investigating clinical utility of cannabinoids in the management of pain show conflicting evidence. While the results of studies investigating therapeutic benefits of cannabinoids in neuropathic pain are promising, 8-11 the effectiveness of cannabinoids in the management of other types pain is unclear. 11-15 Some evidence suggests that cannabinoids are as effective as placebo for treating pain and in some cases may be anti-analgesic when administered at high doses. 12,13,15-17 In light of limited evidence supporting clinical use of cannabis in the management of pain, a
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".