Comparing medical cannabis use in 5 US states: a retrospective database study
Bibliographic record
Abstract
BACKGROUND: US states have been adopting their own medical cannabis laws since 1996. There is substantial variability in the medical cannabis programs between states, and these differences have not been thoroughly investigated in the literature. The objective of the study was to compare medical cannabis patient characteristics across five states to identify differences potentially caused by differing policies surrounding condition eligibility. METHODS: We conducted secondary analyses following a retrospective study of a registry database with data from 33 medical cannabis evaluation clinics in the US, owned and operated by CB2 Insights. This study narrowed the dataset to include patients from five states with the largest samples: Massachusetts (n = 27,892), Colorado (n = 16,434), Maine (n = 4591), Connecticut (n = 2643), and Maryland (n = 2403) to conduct an in-depth study of the characteristics of patients accessing medical cannabis in these states, including analysis of variance to compare average ages and number of conditions and chi-squared tests to compare proportions of patient characteristics between states. RESULTS: Average ages varied between the states, with the youngest average in Connecticut (42.2) and the oldest in Massachusetts (47.0). Males represented approximately 60% of the patients with data on gender in each state. The majority of patients in each state had cannabis experience prior to seeking medical certification. Primary medical conditions varied for each state, with chronic pain, anxiety, and back and neck problems topping the list in varying orders for Massachusetts, Maine, and Maryland. Colorado had 78.7% of patients report chronic pain as their primary condition, and 70.4% of patients in Connecticut reported post-traumatic stress disorder as their primary medical condition. CONCLUSION: This study demonstrated the significant impact that policy has on patients' access to medical cannabis in Massachusetts, Colorado, Maine, Connecticut, and Maryland utilizing real-world data. It highlights how qualifications differ between the five states and brings into question the routes through which patients in states with stricter regulations surrounding eligible conditions choose to seek treatment with cannabis. These patients may turn to alternative treatments, or to the illicit or recreational cannabis markets, where permitted.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".