Predictors of Response to Medical Cannabis for Chronic Pain: A Retrospective Review of Real-Time Observational Data
Bibliographic record
Abstract
Objective: People living with chronic pain increasingly use medical cannabis for symptom relief. We conducted a retrospective cohort study examining cannabis for chronic pain relief using anonymous archival data obtained from the medicinal cannabis tracking app, Strainprint®. Method: We acquired cannabis utilization data from 741 adults with chronic pain and used multilevel modeling to examine the association of age, sex, type of pain (muscle, joint or nerve pain), cannabis formulation (high CBD, balanced CBD:THC, or high THC), route of administration (inhaled or ingested), cannabis use before vs. during the COVID-19 pandemic, and duration of cannabis use, with pain relief. Results: Most patients were female (n = 464; 63%), with a mean age of 39 (SD = 11), and our cohort had completed a total of 83,622 tracked cannabis sessions through Strainprint. The majority of sessions reported use of inhaled cannabis products (78%), typically with high tetrahydrocannabinol (THC; 64%) versus high cannabidiol (CBD; 15%) or balanced THC:CBD (21%) products. The median change in pain scores across sessions was -3.0 points on a 10-point numeric rating scale (NRS; IQR -4.5 to -2.0). In our adjusted model, greater pain relief was associated with male vs. female sex (-0.69 points on a 10-point NRS; 95%CI -0.46 to -0.91). We found statistically significant, but trivial associations with joint pain (-0.05 points), balanced THC:CBD products in the long term (-0.003 points), and cannabis use during the pandemic (0.18 points). Conclusions: We found that people living with chronic pain report important pain relief when using cannabis for medical purposes, and that men may achieve greater pain relief than women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 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".