The Quebec Cannabis Registry: a pharmacovigilance and effectiveness study on the use of medical cannabis in cancer patients.
Bibliographic record
Abstract
12109 Background: The Quebec Cannabis Registry (QCR) was launched in 2015 to allow physicians to prescribe medical cannabis (MC) in the province of Quebec, Canada. This study aimed to investigate the safety and effectiveness of MC in cancer patients using pharmacovigilance data prospectively collected for up to 24 months. Methods: Patients were enrolled in the QCR between May 2015 and October 2018 and followed every 3 months. Study outcomes included adverse events (AE), pain severity and interference (Brief-Pain Inventory), wellbeing (Revised-Edmonton Symptom Assessment Scale) and overall health scale (EQ5D5L) at baseline and at each follow-up (F-UP). Significance of changes over time were assessed using repeated-measures ANOVA. Results: Out of the 2991 patients enrolled in the QCR, 358 (12.8%) were cancer patients (mean age 57.7 (± 14.6); 171 (47.8%) males). The main cancer types were breast (16.2%), lung (11.7%), leukemia (11.5%) and colorectal (11.2%). MC was prescribed primarily for pain (72.1%), anxiety (4.7%), nausea (4.5%), anorexia (3.9%), and insomnia (3.1%). A total of 13 patients (3.6%) reported AE with only three being serious (one unrelated to MC: stroke; and two possibly related: diarrhea, from CBD oil overdose and pneumonia from smoking MC). Mean scores significantly (p < 0.05) improved between baseline and 3 months F-UP for pain severity (4.8 ± 1.5 vs 4.1 ± 1.8), pain interference (4.6 ± 1.8 vs 3.8 ± 1.7), and the overall health scale (60 ± 21 vs 71 ± 18). Well-being scores also significantly improved between baseline and 6 months F-UP (4.4 ± 2.1 vs 3.5 ± 2.8). Conclusions: Population-based data shows that cancer patients can benefit safely and effectively from MC as a complementary treatment, when prescribed and monitored under medical-nursing supervision.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".