Medical cannabis is effective for cancer-related pain: Quebec Cannabis Registry results
Bibliographic record
Abstract
OBJECTIVES: To evaluate the safety and effectiveness of medical cannabis (MC) in reducing pain and concurrent medications in patients with cancer. METHODS: This study analysed data collected from patients with cancer who were part of the Quebec Cannabis Registry. Brief Pain Inventory (BPI), revised Edmonton Symptom Assessment System (ESAS-r) questionnaires, total medication burden (TMB) and morphine equivalent daily dose (MEDD) recorded at 3-month, 6-month, 9-month and 12-month follow-ups were compared with baseline values. Adverse events were also documented at each follow-up visit. RESULTS: This study included 358 patients with cancer. Thirteen out of 15 adverse events reported in 11 patients were not serious; 2 serious events (pneumonia and cardiovascular event) were considered unlikely related to MC. Statistically significant decreases were observed at 3-month, 6-month and 9-month follow-up for BPI worst pain (5.5±0.7 baseline, 3.6±0.7, 3.6±0.7, 3.6±0.8; p<0.01), average pain (4.1±0.6 baseline, 2.4±0.6, 2.3±0.6, 2.7±0.7; p<0.01), overall pain severity (3.7±0.5 baseline, 2.3±0.6, 2.3±0.6, 2.4±0.6; p<0.01) and pain interference (4.3±0.6 baseline, 2.4±0.6, 2.2±0.6, 2.4±0.7, p<0.01). ESAS-r pain scores decreased significantly at 3-month, 6-month and 9-month follow-up (3.7±0.6 baseline, 2.5±0.6, 2.2±0.6, 2.0±0.7, p<0.01). THC:CBD balanced strains were associated with better pain relief as compared with THC-dominant and CBD-dominant strains. Decreases in TMB were observed at all follow-ups. Decreases in MEDD were observed at the first three follow-ups. CONCLUSIONS: Real-world data from this large, prospective, multicentre registry indicate that MC is a safe and effective complementary treatment for pain relief in patients with cancer. Our findings should be confirmed through randomised placebo-controlled trials.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".