Gaps in evidence for the use of medically authorized cannabis: Ontario and Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: With legal access to medical cannabis in Canada since 2001, there is a need to fully characterize its use at both the individual and population levels. We draw on data from Canada's largest cohort study of medical cannabis to identify the primary reasons for medical cannabis authorization in Canada from 2014 to 2019 in two major provinces: Alberta (AB) and Ontario (ON), and review the extent that evidence supports each indication. METHODS: Self-reported baseline assessments were collected from adult patients in ON (n = 61,835) and AB (n = 3410) who were authorized medical cannabis. At baseline, sociodemographic, primary medical information, and validated clinical questionnaires were completed by patients as part of an individual assessment. Patients' reasons for seeking medical cannabis were compared to published reviews and guidelines to assess the level of evidence supporting medical cannabis use for each condition. RESULTS: Medical cannabis use in both AB and ON was similar in both demographic and reason for authorization. The most common reasons for medical cannabis authorization were: (1) pain (AB = 77%, ON = 76%) primarily due to chronic musculoskeletal, arthritic, and neuropathic pain, (2) mental health concerns (AB = 32.9%, ON = 38.7%) due to anxiety and depression, and (3) sleep problems (AB = 28%, ON = 25%). More than 50 other conditions were identified as reasons for obtaining authorization. CONCLUSION: In both AB and ON, the majority of reasons for medical cannabis authorization are not substantiated by clinical evidence to fully support its efficacy for long-term use. Ongoing epidemiological studies on medical cannabis on these treatments are warranted to fully outline its treatment benefits or risks.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".