Consensus-Based Recommendations for Titrating Cannabinoids and Tapering Opioids for Chronic Pain Control
Bibliographic record
Abstract
Aims: Opioid misuse and overuse has contributed to a widespread overdose crisis and many patients and physicians are considering medical cannabis to support opioid tapering and chronic pain control. Using a five-step modified Delphi process, we aimed to develop consensus-based recommendations on: 1) when and how to safely initiate and titrate cannabinoids in the presence of opioids, 2) when and how to safely taper opioids in the presence of cannabinoids, and 3) how to monitor patients and evaluate outcomes when treating with opioids and cannabinoids. Results: In patients with chronic pain taking opioids not reaching treatment goals, there was consensus that cannabinoids may be considered for patients experiencing or displaying opioid-related complications, despite psychological or physical interventions. There was consensus observed to initiate with a CBD-predominant oral extract in the daytime and consider adding THC. When adding THC, start with 0.5–3 mg, and increase by 1–2 mg once or twice weekly up to 30–40 mg/day. Initiate opioid tapering when the patient reports a minor/major improvement in function, seeks less as-needed medication to control pain, and/or the cannabis dose has been optimized. The opioid tapering schedule may be 5%–10% of the morphine equivalent dose (MED) every 1 to 4 weeks. Clinical success could be defined by an improvement in function/quality of life, a ≥ 30% reduction in pain intensity, a ≥ 25% reduction in opioid dose, a reduction in opioid dose to < 90 mg MED, and/or reduction in opioid-related adverse events. Conclusions: This five-stage modified Delphi process led to the development of consensus-based recommendations surrounding the safe introduction and titration of cannabinoids in concert with tapering opioids.
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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 | 0.227 | 0.322 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.013 | 0.016 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".