Perceptions in orthopedic surgery on the use of cannabis in treating pain: a survey of patients with spine pain (POSIT Spine)
Bibliographic record
Abstract
BACKGROUND: Back pain is the leading cause of disability worldwide. Despite guidelines discouraging opioids as first-line treatment, opioids remain the most prescribed drugs for back pain. There is renewed interest in exploring the potential medical applications of cannabis, and with the recent changes in national legislation there is a unique opportunity to investigate the analgesic properties of cannabis. METHODS: This was a multi-center survey-based study examining patient perceptions regarding cannabis for spine pain. We included patients presenting with back or neck pain to one of three Orthopedic clinics in Ontario. Our primary outcome was perceived effect of cannabis on back pain, while secondary outcomes were perceptions regarding potential applications and barriers to cannabis use. RESULTS: 259 patients participated in this study, 35.3% (90/255) stating they used cannabis medically. Average pain severity was 6.5/10 ± 0.3 (95% CI 6.2-6.8). Nearly three-quarters were prescribed opioids (73.6%, 148/201), with oxycodone/oxycontin (45.9% 68/148) being the most common, and almost half of (49.3%, 73/148) had used an opioid in the last week. Patients estimated cannabis could treat 54.3% ± 4.0 (95% CI 50.3-58.3%) of their spine pain and replace 46.2% ± 6. 6 (95% CI 39.6-52.8%) of their current analgesics. Age (β = - 0.3, CI - 0.6-0.0), higher pain severity (β = 0.4, CI 0.1-0.6) and previous cannabis use (β = 14.7, CI 5.1-24.4) were associated with a higher perceived effect of cannabis. Patients thought cannabis would be beneficial to treat pain (129/146, 88.4%), and reduce (116/146, 79.5%) or eliminate opioids (102/146, 69.9%). Not considering using cannabis for medical purposes (65/150, 43.3%) was the number one reported barrier. CONCLUSIONS: Patients estimated medical cannabis could treat more than half of their spine pain, with one in three patients already using medical cannabis. 79% of patients also believe cannabis could reduce opioid usage. This data will help support more research into cannabis for musculoskeletal pain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".