Medical Cannabis for Patients Over Age 50: A Multi-site, Prospective Study of Patterns of Use and Health Outcomes
Bibliographic record
Abstract
Objective: Cannabis is being used as a therapeutic option by patients around the globe, and older patients represent a rapidly growing subset of this population. This study aims to assess the patterns of medical cannabis use in patients over 50 years of age and its effect on health outcomes such as pain, sleep, quality of life, and co-medication. Method: The Medical Cannabis in Older Patients Study (MCOPS) is a multi-site, prospective observational study examining the real-world impact of medical cannabis use on patients over age 50 under the guidance of a health care provider. The study included validated instruments, with treating physicians collecting detailed data on participant characteristics, medical cannabis and co-medication use, and associated impacts on pain, sleep, quality of life, as well as adverse events. Results: Inclusion criteria were met by 299 participants. Average age of participants was 66.7 years, and 66.2% of respondents identified as female. Approximately 90% of patients used medical cannabis to treat pain-related conditions such as chronic pain and arthritis. Almost all patients reported a preference for oral cannabis products (e.g., extracts, edibles) rather than inhalation products (e.g., flower, vapes), and most preferred oral formulations high in cannabidiol and low in tetrahydrocannabinol. Over the six-month study period, significant improvements were noted in pain, sleep, and quality of life measures, with 45% experiencing a clinically meaningful improvement in pain interference and in sleep quality scores. Additionally, nearly 50% of patients taking co-medications at baseline had reduced their use by the end of the study period, and quality of life improved significantly from baseline to M3 and from baseline to M6, with an incremental cost per quality-adjusted life-year (QALY) of $25,357.20. No serious adverse events (SAEs) were reported. Conclusions: In this cohort of older patients, most of whom suffered from pain-related conditions, medical cannabis seemed to be a safe and effective treatment. Most patients experienced clinically significant improvements in pain, sleep, and quality of life and reductions in co-medication. The cost per QALY was well below the standard for traditional pharmaceuticals, and no SAEs were reported, suggesting that cannabis is a relatively safe and cost-effective therapeutic option for adults dealing with age-related health conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".