The Efficacy of Medical Marijuana in the Treatment of Cancer-Related Pain
Bibliographic record
Abstract
Background: The opioid epidemic has spurred investigations for nonopioid options, yet limited research persists on medical marijuana's (MMJ) efficacy in managing cancer-related symptoms. Objective: We sought to characterize MMJ's role on symptomatic relief and opioid consumption in the oncologic population. Design: Retrospective chart review of MMJ-certified oncology patients was performed. Divided patients into MMJ use [MMJ(+)] versus no use [MMJ(−)], and Edmonton Symptom Assessment System (ESAS)-reported pain cohorts: “mild-moderate” versus “severe.” Measurements: Medical records were reviewed for ESAS, to measure physical and emotional symptoms, and opiate consumption, converted into morphine milligram equivalents (MME). Minimal clinically important differences were determined. Wilcoxon signed-rank tests determined statistical significance between MMJ-certification and most recent palliative care visit. Results: Identified 232 patients [95/232 MMJ(−); 137/232 MMJ(+)]. Pain, physical and total ESAS significantly improved for total MMJ(−) and MMJ(+); however, only MMJ(+) significantly improved emotional ESAS. MMJ(−) opioid consumption increased by 23% (97.5–120 mg/day MME, p = 0.004), while it remained constant (45–45 mg/day MME, p = 0.522) in MMJ(+). Physical and total ESAS improved in mild-moderate-MMJ(−) and MMJ(+). Pain and emotional symptoms worsened in MMJ(−); while MMJ(+)'s pain remained unchanged and emotional symptoms improved. MMJ(−) opioid consumption increased by 29% (90–126 mg/day MME, p = 0.012); while MMJ(+)'s decreased by 33% (45–30 mg/day MME, p = 0.935). Pain, physical, emotional, and total ESAS scores improved in severe-MMJ(−) and MMJ(+); opioid consumption reduced by 22% in MMJ(−) (135–106 mg/day MME, p = 0.124) and 33% in MMJ(+) (90–60 mg/day MME, p = 0.421). Conclusions: MMJ(+) improved oncology patients' ESAS scores despite opioid dose reductions and should be considered a viable adjuvant therapy for palliative management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".