Cannabis and cannabinoid medications for the treatment of chronic orofacial pain: A scoping review
Bibliographic record
Abstract
To collate and summarize existing evidence for the use of cannabis and cannabinoids to treat chronic orofacial pain (COP) by oral and maxillofacial surgeons (OMFS), oral medicine specialists (OMS), and orofacial pain specialists (OPS). We systematically screened for sources including a measure of effect of a cannabinoid compound on pain in COP patients that might be treated by our target specialists. Sources were selected by two authors independently. Sources were summarized by country, publication date, objective(s), COP condition(s) studied, cannabinoid(s) studied, methods, results, limitations, and conclusions. A thematic analysis and word cloud were conducted to elucidate commonalities, emphases, and gaps amongst identified sources. Retrieved from MEDLINE, EMBASE, Web of Science Core Collections, Dentistry and Oral Sciences, DARE, CCRCT, and US National Institute of Health and Controlled Trials Register. Of 705 retrieved titles, 8 met inclusion/exclusion criteria and were included for review. Included sources dealt with COP attributed to: head and neck cancer (3), multiple sclerosis-related trigeminal neuralgia-like symptoms (2), post-herpetic neuralgia (1), temporomandibular dysfunction (1), and primary burning mouth syndrome (1). Cannabinoids studied included: self-administered cannabis (3), topical N-palmitoyle-thanolamine (1), topical cannabis extract (1), cannabis sativa oil (1), nabiximols oromucosal spray (1), and nabilone (1). Most sources concluded their respective cannabinoid treatments to provide some therapeutic benefit for COP (6 of 8) and all concluded their treatments to be safe. Current research is wholistically focused, recording outcome measures for pain, anxiety, depression, quality of life, functional disability. Cannabinoids are most often studied as adjunctive and palliative treatments. Cannabinoids are becoming increasingly accessible and might benefit many COP patients. Patients and clinicians require more and higher quality evidence to make confident and informed decisions regarding treatment of COP with cannabis or cannabinoids. This review summarizes current evidence for patients, clinicians, and future researchers.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".