Pain Management for the Young Adult Rheumatology Patient in an Era of Medicinal Marijuana Legalization
Bibliographic record
Abstract
The young adult (18 to 23 years old) rheumatology patient has unique needs. While transitioning through a life stage that encompasses physical and emotional changes, these patients are also dealing with the challenges of coping with the many facets of chronic illness. Pain, a prevalent symptom of rheumatic disease, impairs physical, social, and emotional function, requiring a pain-directed treatment plan in parallel with disease modification. In the current context of the medical legalization of marijuana (herbal cannabis) worldwide, many young patients may be looking to this compound as a pain management option. Familiarity with marijuana as a recreational product that is socially acceptable for many youth may encourage therapeutic use, smudging the lines between recreational and medicinal use. Therefore the medical community must be knowledgeable about current evidence of benefits and risks of marijuana as a therapeutic option to competently advise these young people. Principles of pain management, generic to all pain conditions, should incorporate nonpharmacologic measures as a first step, with attention to good lifestyle habits, education to strengthen the therapeutic alliance, and promotion of coping strategies1. Without providing a detailed description of current pharmacologic treatments for chronic pain, any choice for the young adult must especially take into consideration effects on cognition, potential effects on the developing brain, and interactions with other substances. The pharmacologic treatment of pain should begin with use of the simple analgesics and thereafter with nonsteroidal antiinflammatory drugs (NSAID). Compliance with a regimen of continuous medication, especially with more frequent dosing as for some NSAID, remains a challenge. While not denying the need for pain management, the evidence to support opioid use to treat rheumatic pain is scant. Opioids should therefore be reserved for those with severe pain unresponsive to standard measures, and ideally prescribed for short time periods. Opioids are addictive. … Address correspondence to Dr. M.A. Fitzcharles, Montreal General Hospital, 1650 Cedar Ave., Montreal, Quebec H3G 1A4, 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".