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 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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".