Concomitant use of medical cannabis and drugs associated with risks of interaction in older patients: a longitudinal cohort study
Bibliographic record
Abstract
BACKGROUND: Cannabinoids interact with multiple drugs, with some interactions having significant clinical effects. OBJECTIVES: This study aimed to evaluate amongst older people (i) the trends in the concomitant medical use of cannabis and drugs associated with a risk of significant clinical interaction with cannabinoids (DARSCIC), including those with a narrow therapeutic index (drugs with a narrow therapeutic index; DNTI) such as warfarin, and (ii) the risk of bleeding, drug-related intoxication, thyrotoxicosis and major cardiovascular events. METHODS: For objective 1, we conducted an interrupted time series study amongst 12 599 seniors who received an authorised cannabis prescription in Ontario from 2014-2019. Using clinical and medico-administrative data, dispensations of DARSCIC were assessed pre-post cannabis prescription. For objective 2, longitudinal cohort studies were conducted in patients concomitantly exposed to cannabis and warfarin (bleeding) or to DNTI (intoxication) versus controls. RESULTS: For objective 1, the trends of DARSCIC/DNTI dispensations were similar in the year before and after cannabis prescription. For objective 2, amongst 378 patients exposed to cannabis and warfarin, the risk of bleeding was 1.19, 95%CI (0.71-1.98), compared to 1646 controls. The risk of drug-related intoxication was 2.61, 95%CI (1.42-4.79) amongst 3926 patients exposed to cannabis and DNTI compared to 12 223 controls. Patients exposed to cannabis and levothyroxine (n = 2499) had a significantly higher risk of heart failure but not thyrotoxicosis, acute coronary syndrome or stroke. CONCLUSION: These findings indicate that prescribing practices for cannabis may not adequately consider the implications of DARSCIC/DNTI. Enhancing prescriber awareness of these interactions could mitigate the risk of adverse effects such as drug-related intoxication.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".