Evaluation of Patient Reported Safety and Efficacy of Cannabis From a Survey of Medical Cannabis Patients in Canada
Bibliographic record
Abstract
With the medical use of cannabis permitted in Canada since 2001, patients seek to use this botanical drug to treat a range of medical conditions. However, many healthcare practitioners express the need for further scientific evidence around the use of medical cannabis. This real-world evidence study aimed to address the paucity of scientific data by surveying newly registered medical cannabis patients, before beginning medical cannabis treatment, and at one follow up 6 weeks after beginning medical cannabis treatment. The goal was to collect data on efficacy, safety and cannabis product type information to capture the potential impact medical cannabis had on patient-reported quality of life (QOL) and several medical conditions over a 6-week period using validated questionnaires. The 214 participants were mainly male (58%) and 57% of the population was older than 50. The most frequently reported medical conditions were recurrent pain, post-traumatic stress disorder (PTSD), anxiety, sleep disorders [including restless leg syndrome (RLS)], and arthritis and other rheumatic disorders. Here we report that over 60% of our medical cannabis cohort self-reported improvements in their medical conditions. With the use of validated surveys, we found significant improvements in recurrent pain, PTSD, and sleep disorders after 6 weeks of medical cannabis treatment. Our findings from patients who reported arthritis and other rheumatic disorders are complex, showing improvements in pain and global activity sub-scores, but not overall changes in validated survey scores. We also report that patients who stated anxiety as their main medical condition did not experience significant changes in their anxiety after 6 weeks of cannabis treatment, though there were QOL improvements. While these results show that patients find cannabis treatment effective for a broad range of medical conditions, cannabis was not a remedy for all the conditions investigated. Thus, there is a need for future clinical research to support the findings we have reported. Additionally, while real-world evidence has not historically been utilized by regulatory bodies, we suggest changes in public policy surrounding cannabis should occur to reflect patient reported efficacy of cannabis from real-world studies due to the uniqueness of medical cannabis's path to legalization.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".