Problematic Use of Prescription Opioids and Medicinal Cannabis Among Patients Suffering from Chronic Pain
Bibliographic record
Abstract
Objective: To assess prevalence rates and correlates of problematic use of prescription opioids and medicinal cannabis (MC) among patients receiving treatment for chronic pain. Design: Cross-sectional study. Setting: Two leading pain clinics in Israel. Subjects: Our sample included 888 individuals receiving treatment for chronic pain, of whom 99.4% received treatment with prescription opioids or MC. Methods: Problematic use of prescription opioids and MC was assessed using DSM-IV criteria, Portenoy’s Criteria (PC), and the Current Opioid Misuse Measure (COMM) questionnaire. Additional sociodemographic and clinical correlates of problematic use were also assessed. Results: Among individuals treated with prescription opioids, prevalence of problematic use of opioids according to DSM-IV, PC, and COMM was 52.6%, 17.1%, and 28.7%, respectively. Among those treated with MC, prevalence of problematic use of cannabis according to DSM-IV and PC was 21.2% and 10.6%, respectively. Problematic use of opioids and cannabis was more common in individuals using medications for longer periods of time, reporting higher levels of depression and anxiety, and using alcohol or drugs. Problematic use of opioids was associated with higher self-reported levels of pain, and problematic use of cannabis was more common among individuals using larger amounts of MC. Conclusions: Problematic use of opioids is common among chronic pain patients treated with prescription opioids and is more prevalent than problematic use of cannabis among those receiving MC. Pain patients should be screened for risk factors for problematic use before initiating long-term treatment for pain-control.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".