A population-based study of medical cannabis utilization in patients with cancer.
Bibliographic record
Abstract
124 Background: Between 2014 and 2018, access to cannabis for medical purposes was regulated by Health Canada. Surveys have demonstrated that cancer patients use cannabis to manage symptoms and side effects. Medical cannabis utilization patterns in cancer patients under Canada’s regulatory framework have not been well-described. We aimed to determine the proportion of cancer patients who used medical cannabis, timing of use in relation to cancer treatment and sociodemographic factors predicting use. Methods: The Alberta Cancer Registry was used to identify all patients age ≥ 18 diagnosed with invasive cancer in the province from April 01, 2014 to December 31, 2016. These cases were linked to records from the College of Physicians and Surgeons of Alberta database which collects data on patients who received an authorization for medical cannabis. Authorization was used as a surrogate for medical cannabis utilization. Univariate and multivariate logistic regression models were constructed to determine factors associated with medical cannabis utilization. Results: We identified 41,889 patients between April 1, 2014 and December 31, 2016. Median age at cancer diagnosis was 65 and 50% were female. Among these patients, 1,070 (2.5%) used medical cannabis. Of these patients, 541 (51%) used medical cannabis within 1 year of diagnosis, 248 (52%) within one year of the start of systemic therapy and 128 (41%) within one year of the start of radiation therapy. On multivariate analysis, patients aged 18-29 (OR 12.4, 95% CI 7.8-19.7) and those receiving systemic therapy (OR 2.0, 95% CI 1.7-2.4) were more likely to use medical cannabis (p < 0.001). There were 171 unique physicians who authorized medical cannabis of which only 3.5% (6/171) were oncologists. Conclusions: A small proportion of cancer patients used medical cannabis under Health Canada’s regulatory framework. Utilization was associated with a cancer diagnosis and receiving treatment. Younger patients and those undergoing systemic treatment were predictors of medical cannabis use. Further study is required to understand utilization patterns after cannabis legalization and how to incorporate these findings into patient-centered cancer care.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".