Medical cannabis authorization patterns, safety, and associated effects in older adults
Bibliographic record
Abstract
BACKGROUND: Use of medical cannabis is increasing among older adults. However, few investigations have examined cannabis use in this population. METHODS: We assessed the authorization patterns, safety, and effects of medical cannabis in a sub-analysis of 201 older adults (aged ≥ 65 years) who completed a 3-month follow-up during this observational study of patients who were legally authorized a medical cannabis product (N = 67). Cannabis authorization patterns, adverse events (AEs), Edmonton Symptom Assessment Scale-revised (ESAS-r), and Brief Pain Inventory Short Form (BPI-SF) data were collected. RESULTS: The most common symptoms for which medical cannabis was authorized were pain (159, 85.0%) and insomnia (9, 4.8%). At baseline and at the 3-month follow-up, cannabidiol (CBD)-dominant products were authorized most frequently (99, 54%), followed by balanced products (76, 42%), and then delta-9-tetrahydrocannabinol (THC)-dominant products (8, 4.4%). The most frequent AEs were dizziness (18.2%), nausea (9.1%), dry mouth (9.1%), and tinnitus (9.1%). Significant reductions in ESAS-r scores were observed over time in the domains of drowsiness (p = .013) and tiredness (p = .031), but not pain (p = .106) or well-being (p = .274). Significant reductions in BPI-SF scores over time were observed for worst pain (p = .010), average pain (p = .012), and overall pain severity (p = 0.009), but not pain right now (p = .052) or least pain (p = .141). CONCLUSIONS: Overall, results suggest medical cannabis was safe, well-tolerated, and associated with clinically meaningful reductions in pain in this sample of older adults. However, the potential bias introduced by the high subject attrition rate means that all findings should be interpreted cautiously and confirmed by more rigorous studies.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".