Potential Impact of Medical Cannabis Treatment On Pain Control Among Cancer Patients in Quebec U2013 Canada : a Pilot Study
Bibliographic record
Abstract
Introduction: Therapeutic applications of medical cannabis within the cancer population, particularly for chemotherapy induced peripheral neuropathy, and how this complementary approach can impact patientsu2019 quality of life are still under-investigated.Methods: The Cannabis Pilot Project (CPP) accepted McGill University Health Centre cancer patients already receiving supportive care but referred to the CPP because they did not achieve adequate symptom relief. This study examines the efficacy of cannabis treatment for pain relief using the Brief Pain Inventory (BPI) scale. An interdisciplinary team was established to systematically assess patients, prescribe and monitor cannabis treatments. Results: Sixty-five patients have been enrolled (mean age 61 years; 52% female) in the CPP over seven months. The comparison between baseline, first (n=45) and second (n= 27) follow-up showed that 41% of patients vs 50% improved for worst pain (BPI 5.56 vs 4.39 vs 3.76, p-value 0.030), 41% vs 59% for average pain (BPI 3.93 vs 3.26 vs 2.72), 46% vs 45% for interference pain (BPI 4.05 vs 2.19 vs 2.80). Around 15% of patients reported mild adverse events at both follow-ups (i.e. light headedness in the morning). Conclusion: Cannabis treatment seems to be safe and effective for cancer pain improvement. Fifty percent of patients improved both clinically and statistically for worst pain across the three visits. Large longitudinal studies may confirm stronger correlations between longer exposure to cannabis and pain relief.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".