Bibliographic record
Abstract
INTRODUCTION: Cannabis continues to garner attention due to increasing legalization globally and its potential effects on public health. Consequently, dental practitioners should be familiar with its impacts on oral health and how to properly manage these impacts in the clinical setting. This review explores the connection between cannabis use and oral health, providing insights into national usage trends, cannabis pharmacology, potential oral health impacts and important considerations for patient management. METHODS: We compiled data on self-reported cannabis use from the Canadian Cannabis Survey (2017-2024) and analyzed changes in cannabis use over time with a 2-tailed z-test. We performed a scoping literature review using multiple databases to examine cannabis pharmacology, dentally relevant drug interactions, oral health effects and dental management strategies. RESULTS: Following legalization in 2018, cannabis use in Canada increased by 3.8 percentage points (p < 0.001). Although cannabis may have some dentally relevant drug interactions, the published data are not conclusive, and further investigation is required. Cannabis may be associated with xerostomia, may promote caries development, may negatively affect the periodontium and may increase the incidence of oral lesions. We used this information to develop a framework to guide clinicians in assessing and treating patients with a history of cannabis use. CONCLUSION: The survey findings suggest that cannabis use is on the rise in Canada, with implications for the provision of dental care. Cannabis may alter the effects of drugs used in the dental setting and may be associated with an increased incidence of oral health issues. The dental management strategies suggested here are intended as an informative reference for clinicians. Additional research is needed to further elucidate the long-term effects of cannabis on oral health, its drug interactions and its potential medicinal applications in dentistry.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.699 | 0.467 |
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".