Opioid Doctor Shopping: A Rare Phenomenon Among Patients with Chronic Non-Cancer Pain Followed in Tertiary Care Settings
Bibliographic record
Abstract
BACKGROUND: Opioid doctor shopping has not yet been investigated in patients followed in tertiary care settings. This study aimed at assessing the prevalence of opioid doctor shopping among patients with chronic non-cancer pain (CNCP) (ie, pain lasting ≥3 months) attending multidisciplinary pain clinics in Quebec, Canada. PATIENTS AND METHODS: This was a retrospective cohort study of patients with CNCP enrolled in the Quebec Pain Registry (QPR) between 2008 and 2014. QPR data were linked to the Quebec health insurance databases. The index date was the date of the first visit at the pain clinic. Prevalence of doctor shopping was assessed within the 12 months following the index date. Doctor shopping was defined as at least 1 day of overlapping opioid prescriptions from ≥2 prescribers and filled in ≥3 pharmacies. RESULTS: A total of 2191 patients with CNCP with at least one opioid dispensation within the 12 months following the index date were included. The mean age was 58.6±14.9 years and 41.3% were men. The median pain duration was 4 years, and 13.3% of patients were diagnosed with neuropathic pain. Regarding past year comorbidities, 15.0% presented anxiety, 16.8% depression and 6.4% substance use disorder. Among the included patients, 15 (0.7%) presented at least one episode of doctor shopping. Among these doctor-shoppers, 9 (60.0%) exhibited only 1 episode. CONCLUSION: Opioid doctor shopping is a rare phenomenon among patients with CNCP treated in tertiary care settings. Opioids should remain a drug option for patients without substance use disorder, and who have persistent pain despite optimized nonopioid therapy.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".