Cohort study of medical cannabis authorisation and healthcare utilisation in 2014–2017 in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The impact of medical cannabis on healthcare utilisation between 2014 and 2017 in Ontario, Canada. With cannabis legalisation in Canada and some states in the USA, high-quality longitudinal cohort research studies are of urgent need to assess the impact of cannabis use on healthcare utilisation. METHODS: A matched cohort study of 9925 medical cannabis authorised adult patients (inhaled (smoked or vaporised) or orally consumed (oils)) at specialised cannabis clinics, and inclusion of 17 732 controls (not authorised) between 24 April 2014 and 31 March 2017 from Ontario, Canada. Interrupted time series and multivariate Poisson regression analyses were conducted. Medical cannabis impact on healthcare utilisation was measured over 6 months: all-cause physician visits, all-cause hospitalisation, ambulatory care sensitive conditions (ACSC)-related hospitalisations, all-cause emergency department (ED) visits and ACSC-related ED visits. RESULTS: For medical cannabis patients compared with controls, there was an initial (within the first month) increase in physician visits (additional 4330 visits per 10 000 patients). However, a numerical reduction was noted over the 6-month follow-up, and no statistical difference was observed (p=0.126). Likewise, in hospitalisations and ACSC ED visits, there was an initial increase (44 per 10 000 people, p<0.05) but no statistical difference after follow-up (p=0.34). Conversely, no initial increase in all-cause ED visits was observed with a slight decrease (19 visits per 10 000 patients, p=0.014) in follow-up. CONCLUSIONS: An initial increase (within first month) in healthcare utilisation may be expected among medical cannabis users that appears to wane over time. Proactive follow-up of patients using medical cannabis is warranted to minimise initial risks to patients and actively assess potential benefits/harms of ongoing 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".