Practical Strategies Using Medical Cannabis to Reduce Harms Associated With Long Term Opioid Use in Chronic Pain
Bibliographic record
Abstract
Background:Chronic non-cancer pain (CNCP) is estimated to affect 20% of the adult population. Current United States and Canadian Chronic non-cancer pain guidelines recommend careful reassessment of the risk-benefit ratio for doses greater than 90 mg morphine equivalent dose (MED), due to low evidence for improved pain efficacy at higher morphine equivalent dose and a significant increase in morbidity and mortality. There are a number of human studies demonstrating cannabis opioid synergy. This preliminary evidence suggests a potential role of cannabis as an adjunctive therapy with or without opioids to optimize pain control. Methods:In 2017, the Canadian Opioid Guidelines Clinical Tool was created to encourage judicious opioid prescribing for CNCP patients and to reevaluate those who have been chronically using high MED. Mirroring this approach, we draw on our clinical experiences and available evidence to create a clinical tool to serve as a foundational clinical guideline for the initiation of medical cannabis in the management of CNCP patients using chronic opioid therapy. Findings:Following principles of harm reduction and risk minimization, we suggest cannabis be introduced in appropriately selected CNCP patients, using a stepwise approach, with the intent of pain management optimization. We use a structured approach to focus on low dose cannabis (namely, THC) initiation, slow titration, dose optimization and frequent monitoring. Conclusion:When low dose THC is introduced as an adjunctive therapy, we observe better pain control clinically with lower doses of opioids, improved pain related outcomes and reduced opioid related harm.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".