Cannabis use disorder in patients with chronic pain: overestimation and underestimation in a cross-sectional observational study in 3 German pain management centres
Bibliographic record
Abstract
ABSTRACT: There are concerns that cannabis use disorder (CUD) may develop in patients with chronic pain prescribed medical cannabis (MC). The criteria for CUD according to the Statistical Manual for Mental Disorders Version 5 (DSM-5) were not developed for the identification of patients using cannabis for therapeutic reasons. In addition, some items of CUD might be attributed to the desire of the patient to relieve the pain. Therefore, alternative strategies are needed to identify the true prevalence of CUD in persons with chronic pain being treated with MC. The prevalence of CUD in patients with chronic pain according to the DSM-5 criteria was assessed using an anonymous questionnaire in 187 consecutive patients attending 3 German pain centres in 2021. Questionnaires were rated as follows: (1) all criteria included, (2) removal of items addressing tolerance and withdrawal, and (3) removal of positive items if associated with the desire to relieve pain. Abuse was assessed by self-report (use of illegal drugs and diversion and illegal acquisition of MC) and urine tests for illegal drugs. Physicians recorded any observation of abuse. Cannabis use disorder according to the DSM-5 criteria was present in 29.9%, in 13.9% when items of tolerance and withdrawal were removed, and in 2.1% when positive behaviour items were removed. In 10.7%, at least 1 signal of abuse was noted. Urine tests were positive for nonprescribed drugs (amphetamines and tranquilizer) in 4.8% of subjects. Physicians identified abuse in 1 patient. In this study, the DSM-5 criteria overestimated and physicians underestimated the prevalence of CUD in patients prescribed MC for chronic pain.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".