Cannabis Use Prevalence and Correlates in Patients Attending a Canadian Cognitive Clinic
Bibliographic record
Abstract
ABSTRACT Background: Since cannabis was legalized in Canada in 2018, its use among older adults has increased. Although cannabis may exacerbate cognitive impairment, there are few studies on its use among older adults being evaluated for cognitive disorders. Methods: We analyzed data from 238 patients who attended a cognitive clinic between 2019 and 2023 and provided data on cannabis use. Health professionals collected information using a standardized case report form. Results: Cannabis use was reported by 23 out of 238 patients (9.7%): 12 took cannabis for recreation, 8 for medicinal purposes and 3 for both purposes. Compared to non-users, cannabis users were younger (mean ± SD 62.0 ± 7.5 vs 68.9 ± 9.5 years; p = 0.001), more likely to have a mood disorder ( p < 0.05) and be current or former cigarette smokers ( p < 0.05). There were no significant differences in sex, race or education. The proportion with dementia compared with pre-dementia cognitive states did not differ significantly in users compared with non-users. Cognitive test scores were similar in users compared with non-users (Montreal Cognitive Assessment: 20.4 ± 5.0 vs 20.7 ± 4.5, p = 0.81; Folstein Mini-Mental Status Exam: 24.5 ± 5.1 vs 26.0 ± 3.6, p = 0.25). The prevalence of insomnia, obstructive sleep apnea, anxiety disorders, alcohol use or psychotic disorders did not differ significantly. Conclusion: The prevalence of cannabis use among patients with cognitive concerns in this study was similar to the general Canadian population aged 65 and older. Further research is necessary to investigate patients’ motivations for use and explore the relationship between cannabis use and mood disorders and cognitive decline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".