Multiple prescribers in older frequent opioid users--does it mean abuse?
Bibliographic record
Abstract
BACKGROUND: Obtaining analgesic narcotics from multiple prescribers is sometimes called 'doctor-shopping,' implying abuse. If the use of multiple prescribers can be used as an indicator for abuse, it would be a convenient way to study abuse in large populations. OBJECTIVE: To assess multiple prescribers as an indicator of abuse by relating quantity of opioids obtained by older Norwegians to number of prescribers. METHODS: Data were obtained from the Norwegian Prescription database which includes all prescriptions filled in Norwegian pharmacies. The study population consisted of people aged 70-89 who filled five or more prescriptions for weak or for strong opioids in 2008. RESULTS: In 2008, 4,268 persons filled five or more prescriptions for strong opioids and 19,675 for weak opioids. More than 30% had three or more prescribers. Over half of strong opioids users and 72% of weak opioid users had medication-use-periods of over 40 weeks. For strong opioids, increasing DDDs/week was found with increasing number of prescribers. When cancer/palliative care patients were excluded, the mean DDDs/week level for strong opioids was much lower, and little association with number of prescribers remained. For weak opioids, little association between mean DDDs/week and number of prescribers was found. CONCLUSIONS: This study demonstrated that the increasing quantities of strong opioids with increasing number of prescribers are largely due to treatment of cancer/palliative care patients. While the use of multiple prescribers can be a red flag for problematic medication use, it cannot be considered synonymous with 'doctor-shopping' or abuse.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".