Bibliographic record
Abstract
Pain is a prevalent symptom for rheumatology patients. Even when inflammatory arthritis is well controlled, remaining pain or comorbid fibromyalgia is a cause of persistent suffering. As current treatments for pain management are suboptimal, patients are increasingly exploring medical cannabis as a treatment option, with interest bolstered by legalization of both medical and recreational cannabis is many jurisdictions. This easier access to cannabis may even prompt some patients to experiment with use and self-medicate. Although the clinical evidence for effect of cannabinoids in rheumatology management is mostly lacking, rheumatologists must be sufficiently knowledgeable to provide patients with evidence-based information about effects and harms. This review will address the pharmacological properties of medical cannabis, products available, and methods of administration and will highlight considerations applicable for use in various rheumatology patient populations. Medical cannabis may provide some symptom relief for some rheumatology patients, but with caution about known short-term risks and largely unknown long-term risks. Medical cannabis may finally emerge as a treatment option for these patients. Even in the present setting of limited evidence, clinicians must understand the popular advocacy for medical cannabis and play an active role to ensure competent and safe patient care. Keywords: Rheumatic pain. Cannabinoids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".