Rates of Cannabis Use in Patients with Cancer
Bibliographic record
Abstract
Background: A comprehensive assessment of cannabis use by patients with cancer has not previously been reported. In this study, we aimed to characterize patient perspectives about cannabis and its use. Methods: An anonymous survey about cannabis use was offered to patients 18 years of age and older attending 2 comprehensive and 2 community cancer centres, comprising an entire provincial health care jurisdiction in Canada (ethics id: hreba-17011). Results: Of 3138 surveys distributed, 2040 surveys were returned (65%), with 1987 being sufficiently complete for analysis (response rate: 63%). Of the respondents, 812 (41%) were less than 60 years of age; 45% identified as male, and 55% as female; and 44% had completed college or higher education.Of respondents overall, 43% reported any lifetime cannabis use. That finding was independent of age, sex, education level, and cancer histology. Cannabis was acquired through friends (80%), regulated medical dispensaries (10%), and other means (6%). Of patients with any use, 81% had used dried leaves.Of the 356 patients who reported cannabis use within the 6 months preceding the survey (18% of respondents with sufficiently complete surveys), 36% were new users. Their reasons for use included cancer-related pain (46%), nausea (34%), other cancer symptoms (31%), and non-cancer-related reasons (56%). Conclusions: The survey demonstrated that prior cannabis use was widespread among patients with cancer (43%). One in eight respondents identified at least 1 cancer-related symptom for which they were using cannabis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".