Safety and effectiveness of medical cannabis as a complementary option for supportive cancer care: Results from the Cannabis Pilot Project.
Bibliographic record
Abstract
12106 Background: Access to medical cannabis (MC) is a common request by patients and caregivers in supportive cancer care (SCC). However, healthcare professionals require more evidence on MC safety and effectiveness. Methods: The Cannabis Pilot Project (CPP) was implemented at the Cedars Cancer Centre of the McGill University Health Centre to evaluate MC as a complementary option for symptom control in SCC. Referral to the CPP was reserved for patients who were receiving SCC but had not obtained adequate symptom relief. An interdisciplinary team (physician, nurse and research coordinator) was established to systematically assess patients, prescribe and monitor MC treatments and record data on their safety and effectiveness. Patients were enrolled in the CPP between February 2018 and December 2019 and reassessed at intervals of one to six months. Results: Ninety-six cancer patients (mean age 60.0y (±13.9); 41 (42.7%) males) had at least one follow-up (FUP) and were included in the study. The main cancer types were breast (19.8%), lung (9.4%) and colorectal (9.4%). Adverse events (top three: drowsiness, low energy and nausea) were reported in 28% of patients, with 9% having to stop MC. Mean Brief Pain Inventory scores significantly improved between baseline, FUP-2 and FUP-3 for worst pain (5.4± SEM 0.3 vs 4.3±0.3 and 3.7±0.4) and average pain severity (4.2±0.2 vs 3.2±0.3 and 3.2±0.4). Anorexia improved (3.4±0.3 vs 2.2±0.4 and 1.7±0.4), as measured via the revised Edmonton Symptom Assessment System (ESAS-r). ESAS-r wellbeing improved significantly between baseline and FUP-1 (4.4±0.2 vs 3.7±0.2). Between baseline and each FUP, approximately a third of patients dropped their use of concurrent medications (including analgesics, antidepressants and anxiolytics), as measured by the Medication Quantification Scale. Conclusions: The CPP data support the safety and effectiveness of MC as a complementary option for improving pain control, appetite and quality of life in SCC.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".