Perceptions in Orthopedic Surgery on the Use of Cannabis in Treating Pain: A Survey of Musculoskeletal Trauma Patients—Results From the Canadian POSIT Study
Bibliographic record
Abstract
OBJECTIVES: To evaluate the patient-reported expectations regarding cannabis for pain following musculoskeletal (MSK) trauma and patients' perceptions and attitudes regarding its use. DESIGN: A cross-sectional retrospective survey-based study. SETTING: Three orthopaedic clinics in Ontario (Level-1 trauma center, Level-2 trauma center, rehabilitation clinic). PATIENTS SELECTION CRITERIA: Adult patients presenting to the clinics from January 24, 2018, to March 7, 2018, with traumatic MSK injuries (fractures/dislocations and muscle/tendon/ligament injury) were administered an anonymous questionnaire on cannabis for MSK pain. OUTCOME MEASURES AND COMPARISONS: Primary outcome measure was the patients' perceived effect of cannabis on MSK pain, reported on a continuous pain scale (0%-100%, 0 being no pain, and 100 unbearable pain). Secondary outcomes included preferences, such as administration route, distribution method, timing, and barriers (lack of knowledge, concerns for side effects/addiction, moral/religious opposition, etc.) regarding cannabis use. RESULTS: In total, 440 patients were included in this study, 217 (49.3%) of whom were female and 222 (50.5%) were male, with a mean age of 45.6 years (range 18-92 years, standard deviations 15.6). Patients estimated that cannabis could treat 56.5% (95% CI 54.0%-59.0%) of their pain and replace 46.2% (95% CI 42.8%-49.6%) of their current analgesics. Nearly one-third (131/430, 30.5%) reported that they had used medical cannabis and more than one-quarter (123/430, 28.6%) used it in the previous year. Most felt that cannabis may be beneficial to treat pain (304/334, 91.0%) and reduce opioid use (293/331, 88.5%). Not considering using cannabis for their injury (132/350, 37.7%) was the most common reason for not discussing cannabis with physicians. Higher reported pain severity (β = 0.2/point, 95% CI 0.1-0.3, P = 0.005) and previous medical cannabis use were associated with higher perceived pain reduction (β = 11.1, 95% CI 5.4-16.8, P < 0.001). CONCLUSIONS: One in 3 orthopaedic trauma patients used medical cannabis. Patients considered cannabis could potentially be an effective option for managing traumatic MSK pain and believed that cannabis could reduce opioid usage following acute musculoskeletal trauma. These data will help inform clinicians discussing medical cannabis usage with orthopaedic trauma patients moving forward.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".