“Is medical cannabis safe for my patients?” A practical review of cannabis safety considerations
Bibliographic record
Abstract
Medical cannabis use is increasing worldwide. Clinicians are commonly asked by patients to provide guidance on its safety and efficacy. Although there has been an increase in research on the role of medical cannabis for a number of different conditions, we found that there was a paucity of clear safety guidance on its use. We aim to address this issue by answering two pertinent clinician safety questions: 1 Can medical cannabis be safely used in this patient? 2. What strategies can be used to ensure that any harms from medical cannabis are mitigated? To address these questions, we reviewed available evidence and provided expert clinical opinion to summarize the fundamental components for evaluating medical cannabis safety and strategies to reduce risk from its use. Our review resulted in a safety-focused framework for medical cannabis initiation and utilization. We provide clear recommendations for patients being considered for cannabis (e.g. precautions, contraindications and drug interactions). Risk mitigation strategies such as appropriate chemovar (strain) selection, routes of administration, and dosing are reviewed. As with any other pharmacotherapy, we review the key components of monitoring and address potential issues that may arise while using medical cannabis. We propose a structured assessment and monitoring strategy that can be used by clinicians recommending cannabis (CRC) to guide patients through each step of their cannabis journey. This framework can be used to ensure that medical cannabis utilization is associated with the lowest possible risk to the patient.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".