13 - MEDICAL CANNABIS IMPROVES APPETITE AND STABILIZES WEIGHT IN CANCER PATIENTS
Bibliographic record
Abstract
Introduction: Anorexia and weight loss are common side-effects of cancer and its treatments. The efficacy of medical cannabis to improve these symptoms is unclear.Methods: Cancer patients referred to the Cannabis Pilot Project (CPP) of the McGill University Health Centre were included in this study. CPP patients have already received supportive care; however, have not achieved adequate symptom relief with conventional treatments. The Edmonton Symptom Assessment System (ESAS) questionnaire was completed at baseline (BL), visit 1 (>30-75 days after BL) and visit 2 (>75-120 days after BL) to determine improvement in appetite. Weight was available at each visit in a subset of patients.Results: Thirty-seven patients (mean age 61u00b111 y, 51% female) were assessed at BL; of those, 43% reported anorexia as a symptom. Synthetic cannabis was prescribed to 62% of patients. The majority of patients (81%) were prescribed oral cannabis (oil), with 51% receiving Cannabidiol-rich products. There was a significant improvement in appetite over the 3 visits (BL: 3.5u00b13.0; visit 1: 2.2u00b12.4; visit 2: 1.5u00b12.2, p=0.033). Of patients who reported anorexia as a symptom, 75% reported improvement at visit 1, and 80% at visit 2. Weight remained unchanged over time (BL: 70.7u00b119.3 kg; visit 1: 67.4u00b121.5 kg; visit 2: 66.1u00b123.0 kg, p=0.509). Conclusion: Medical cannabis in addition to standard supportive care seems to improve appetite and stabilize weight over time in cancer patients.
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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) | 0.005 | 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".