The Association of Medical Cannabis Use with Pain Levels and Opioid Use in Illinois’ Opioid Alternative Pilot Program
Bibliographic record
Abstract
Objective The state of Illinois’ Opioid Alternative Pilot Program (OAPP) is the first and only official harm-reduction program in the US to address the opioid crisis via facilitation of safe and legal access to medical cannabis. This study evaluates the association of medical cannabis use with pain level and frequency of opioid use in the first cohort of OAPP participants in 2019.Methods A survey was sent OAPP enrollees between February and July 2019. Cannabis users (n = 626) were compared to non-users (n = 234) to determine whether there was an association between cannabis use and self-reported (a) pain level and (b) frequency of opioid use. Backward stepwise regression models were used.Results A total of 860 participants was included in the analysis. Overall, 75% of the study sample reported pain as their primary medical symptom, and 67% of cannabis users reported having a disability. The mean difference in pain level between cannabis users and non-users was 4.5 units (on a 100-point scale) higher among cannabis users than non-users (p = 0.03); and cannabis use was statistically associated with pain level. High-frequency opioid users had lower odds of reporting cannabis use within the past year than low/no opioid users.Conclusions Although there was a statistical association between cannabis use and pain, the difference of 4.5 points in pain level between users and non-users was too small to reflect a clinically meaningful relative difference. This study may provide useful information to providers and clinicians about how the OAPP and similar programs may reduce opioid use and improve health outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".