Use of medical cannabis to reduce pain and improve quality of life in cancer patients.
Bibliographic record
Abstract
198 Background: Early attention to pain and symptoms in those with cancer improves both quality of life and survival. Opioid medications are the mainstay treatment of cancer-related pain. Cannabinoids are increasingly used as adjunctive treatments for cancer pain, but clinical evidence supporting their use as an “opioid sparing agent” or to improve quality of life is as yet unknown. Our study sought to determine if the addition of cannabinoids (medical cannabis) resulted in the reduction of the average opioid dose required for pain control, and improve self-reported quality of life indices. Methods: A retrospective chart review of cancer patients followed in our CCMB Pain and Symptom Clinic was conducted. Inclusion criteria: age over 18 years and formal enrollment in Health Canada’s Marihuana for Medical Purposes (MMPR) program (n = 24). Average dose of opioids were calculated in milligrams of morphine equivalent (ME) per day at the last documented visit prior to enrolment in the MMPR and then at the subsequent clinic visit. Averages of self-reported ESAS scores (pain, tiredness, drowsiness, nausea, appetite, depression, anxiety, sense of wellbeing) were calculated for the same visits. Statistical analysis using the paired student’s t-test compared means and determined the significance of any changes. Results: Following enrolment in the MMPR, the average opioid dose decreased by 70.375mg of MEs (p = 0.29). Self-reported ratings (10-point Likert scale) in pain (0.75, p = 0.23), tiredness (0.58, p = 0.21), drowsiness (1.125, p = 0.04), nausea (1.125, p = 0.04), appetite (1.42, p = 0.04), depression (1.29, p = 0.02) and anxiety (1.58, p = 0.004) improved after enrolment. Sense of wellbeing ratings did not change. Conclusions: Patients with cancer pain benefited from the addition of cannabinoids. The average opioid dose decreased following access to medical cannabis. Self-reported ratings of several quality of life indicators showed statistically significant improvement. Our study shows a signal that cannabinoids may reduce cancer patients’ reliance on opioids to control pain. Further prospective controlled studies are needed to further elucidate the role of cannabinoids in the treatment of cancer pain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".