Cannabis Use in Cancer: A Survey of the Current State at BC Cancer before Recreational Legalization in Canada
Bibliographic record
Abstract
Background: Cancer patients experience multiple symptoms throughout their illness, and some report benefit from the use of cannabis. There are concerns that many patients are accessing products inappropriate for their situation and potentially putting themselves at risk. In the present study, we aimed to capture the prevalence of cannabis use among cancer patients at BC Cancer before recreational legalization in Canada and to identify the reasons that patients take cannabis, the various routes of administration they use, and the reasons that prior users stopped. Methods: Patients were eligible if, on the selected study day (15 August 2018), they were scheduled for an appointment at any of the 6 BC Cancer sites. Eligible patients were mailed a survey. Results: Of surveys sent to 2998 patients, 821 (27.4%) were returned and included in analysis. Of those respondents, 23% were currently using cannabis-based products, almost exclusively for medical purposes, and an additional 28% had been users in the past (most often recreationally). Of the patients currently using cannabis, 31% had medical authorization. The most common symptoms that the current users were targeting were pain, insomnia, nausea, and anxiety; many were also hoping for anticancer effects. Conclusions: More than half the respondents had tried cannabis at some time, and almost one quarter of respondents were currently taking cannabis to help manage their symptoms or treat their cancer, or both. Many more patients would consider use with appropriate guidance from a health care professional. More research is needed to inform physicians and patients about safe uses and doses and about the potential adverse effects of cannabis use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".