Expanding Medical Marijuana Access in Canada: Considerations for the Rheumatologist
Bibliographic record
Abstract
Similar to the enthusiasm for treatment of various ailments that greeted Nicotiana tabacum when brought to Europe from the New World 500 years ago, Cannabis sativa is now hailed as a possible treatment for a multiplicity of symptoms1. Embraced by physicians and the people, from commoners to the aristocracy, the enthusiasm for Nicotiana tabacum was questioned by a discerning few with concerns about the “indiscriminate use of the herb for all diseases in all age groups without specific measured prescriptions”1. Much like for tobacco many years ago, marijuana as the byproduct of Cannabis sativa, with significant financial incentives, requires careful appraisal regarding true therapeutic potential. Cannabinoids are a diverse group of compounds, including in the animal/human system, phytocannabinoids derived from plants, and pharmaceutically prepared synthetocannabinoids. The herbal product is a complexity of over 400 molecules, thereby not allowing for direct comparison or interchangeability with synthetocannabinoids. While the latter are currently available as treatments for some conditions, we will focus on issues pertaining to the use of the phytocannabinoid marijuana (hereafter “herbal cannabis”) in rheumatic diseases. Therapeutic effects have been claimed for various conditions for centuries, but herbal cannabis largely fell into medical disfavor because of variable potency and short shelf life, and was superseded by synthetic analgesic agents that could be reliably dosed and studied2. As pain relief for rheumatic conditions is commonly cited as a reason for medicinal cannabis use, rheumatologists are increasingly caring for patients who are either selfmedicating or requesting advice regarding use. Legislators worldwide are currently debating the merits of expanding access to herbal cannabis for medicinal purposes. In some countries such as Canada, physicians will now be required to write a prescription for herbal cannabis. With increasing pressure from legislators, driven by public advocacy, there exists a discord between … Address correspondence to Dr. Mary-Ann Fitzcharles, Montreal General Hospital, 1650 Cedar Avenue, H3G 1A4, Montreal, Quebec, Canada. E-mail: mary-ann.fitzcharles{at}muhc.mcgill.ca
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.033 | 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".