Dermatology-Related Uses of Medical Cannabis Promoted by Dispensaries in Canada, Europe, and the United States
Bibliographic record
Abstract
There is a growing interest in the use of medical cannabis for a variety of dermatologic conditions. Despite the lack of evidence to validate the effectiveness and safety of marijuana, it is approved to treat a variety of dermatologic conditions in the United States. Furthermore, medical cannabis dispensaries have been making unsubstantiated claims about medical cannabis. It is important for dermatologists to know about the purported use of medical cannabis to help patients navigate this new treatment option, particularly as cannabis becomes legal in Canada in October 2018. We collected and tabulated the dermatologic indications for medical cannabis from Canada, the United States, and Europe. In the United States, dermatologic-approved indications vary by state but include psoriasis, lupus, nail-patella syndrome, and severe pain. Health Canada has listed psoriasis, dermatitis, and pruritus as potential therapeutic uses for cannabis but does not endorse its use for therapeutic purposes. We also surveyed the websites of dispensaries in Canada, the United States, and Europe and found that numerous unsubstantiated claims were being made and advertised to consumers. Dermatologic uses of medical cannabis, as claimed by dispensaries, included treating acne, aging, allergic contact dermatitis, chronic pain, herpes, dermatitis, lupus, Lyme disease, nevi, psoriasis, epidermolysis bullosa, and melanoma. Psoriasis, dermatitis, and chronic pain were the most commonly cited indications for medical cannabis listed by dispensaries. Our data indicate that the suggested and advertised uses of medical cannabis are largely unsubstantiated. Further research is necessary to validate the indications, effectiveness, and safety of medical cannabis.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".