The Risks and Benefits of Cannabis in the Dermatology Clinic
Bibliographic record
Abstract
Cannabis ( Cannabis sativa/indica), also known as marijuana, has been used for medicinal and recreational purposes for millennia. There has been a recent trend to legalize the use of cannabis, as illustrated by the recent legalization votes in numerous states in the United States and legislation in Canada to allow recreational cannabis use. With this increasing consumption of cannabis, dermatologists will see increased pressure to prescribe cannabis and will see the side effects of cannabis use with greater frequency. There are several approved medical indications for cannabis use, including psoriasis, lupus, nail-patella syndrome, and severe pain. In addition, very preliminary studies have suggested cannabis and its derivatives might have use in acne, dermatitis, pruritus, wound healing, and skin cancer. Further well-controlled studies are required to explore these potential uses. Conversely, the side effects of cannabis use are relatively well documented, and dermatologists should be aware of these presentations. Side effects of cannabis use include cannabis allergy manifesting as urticaria and pruritus, cannabis arteritis presenting with necrosis and ulcers, and oral cancers from cannabis smoke. In this review, we summarize some of the studies and reports regarding the medicinal uses of cannabis in the dermatology clinic and some of the side effects that might present more often to dermatologists as the use of cannabis increases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".