The effects of naturalistic cannabis use on cognition and subjective experience in older adults with normal cognitive function
Bibliographic record
Abstract
BACKGROUND: Adults over the age of 65 years are the fastest growing group of cannabis users, but there are few studies of the acute effects of cannabis in this population. AIMS: In the present naturalistic study, participants were invited to smoke their preferred legal cannabis in the lab prior to tests of verbal learning and memory, executive function and information processing, and functional vision and visual attention. METHODS: In this counterbalanced, crossover design, performance after cannabis was compared to a sober condition. Blood levels of delta-9-tetrahydrocannabinol (THC) were measured and correlated with cognition. RESULTS: Participants aged 65-78 (31 in total; 21 male) with normal cognitive function chose to smoke cannabis with an average THC content of 18.7%. Analysis with linear mixed models found that performance on the trail making test versions A and B was decreased 60 minutes after smoking cannabis, compared to the no cannabis condition. The percentage of words retained in the verbal free recall test was significantly higher in the cannabis condition; this was apparently due to a reversal in performance decrement. Smoked cannabis produced robust subjective drug effects. At 60 and 210 minutes, there were unclear correlations of blood THC and metabolites to cognitive performance. CONCLUSIONS: These results suggest that older adults who use cannabis may show decrements in performance on only some tests of cognition, and marked changes in subjective experience.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".