Clinical Profiles of Concurrent Cannabis Use in Chronic Pain: A CHOIR Study
Bibliographic record
Abstract
OBJECTIVE: Despite evidence of the analgesic benefits of cannabis, there remains a relative scarcity of research on the short- and long-term effects of cannabis use in individuals with chronic pain. DESIGN: The current study is a secondary analysis of clinical data from the Collaborative Health Outcomes Information Registry (CHOIR). SETTING: Data were drawn from a cohort of patients of a multidisciplinary tertiary care pain clinic. SUBJECTS: The study sample consisted of data from 7,026 new patient visits from CHOIR; of these, 1,668 patients with a follow-up time point within 180 days were included in a longitudinal analysis. METHODS: Clinical data were analyzed to characterize cross-sectional differences in pain and indicators of psychological and physical function according to self-reported, concurrent cannabis use. Additionally, a propensity score-weighted longitudinal analysis was conducted, examining cannabis use as a predictor of changes in clinical variables across time. RESULTS: Cross-sectional analyses suggested significantly poorer sleep and significantly higher intensities of pain, emotional distress, and physical and social dysfunction in patients reporting ongoing cannabis use; however, these differences were relatively small in magnitude. However, no differences between cannabis users and nonusers in terms of longitudinal changes in clinical variables were noted. DISCUSSION: Our results are among the first to examine concurrent cannabis use as a prognostic variable regarding trajectories of pain-related variables in tertiary care. Future studies may benefit from examining the effect of cannabis initiation, concurrent medication use, and specific aspects of cannabis use (dose, duration of use, or cannabis type) on clinical 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".