Pattern of Opioid Analgesic Prescription for Adults by Dentists in Nova Scotia, Canada
Bibliographic record
Abstract
Global consumption of prescription opioid analgesics has increased dramatically in the past 2 decades, outpacing that of illicit drugs in some countries. The increase has been partly ascribed to the widespread availability of prescription opioid analgesics and their subsequent nonmedical use, which may have contributed to the epidemic of opioid abuse, addiction, and overdose-related deaths. International studies report that dentists may be among the leading prescribers of opioid analgesics, thus adding to the societal impact of this epidemic. Between 2009 and 2011, dentists in the United States prescribed 8% to 12% of opioid analgesics dispensed. There is little information on the pattern of opioid analgesic prescription by dentists in Canada. The aim of this study was to examine the pattern of opioid analgesics prescription by dentists in Nova Scotia (NS), Canada. This retrospective observational study used the provincial prescription monitoring program’s record of oral opioid analgesics and combinations dispensed to persons 16 y and older at community pharmacies that were prescribed by dentists from January 2011 to December 2015. During the study period, more than 70% of licensed dentists in NS wrote a prescription for dispensed opioid analgesics, comprising about 17% of all opioid analgesic prescribers. However, dentists were responsible for less than 4% of all prescriptions for dispensed opioid analgesics, prescribing less than 0.5% of the total morphine milligram equivalent (MMEq) of opioid analgesics dispensed over the 5 y. There was a significant downward trend in total MMEq of dispensed opioid analgesics prescribed by dentists from about 2.23 million MMEq in 2011 to 1.93 million MMEq in 2015 (r = –0.97; P = 0.006). Opioid prescription is common among dentists, but their contribution to the overall availability of opioid analgesics is low. Furthermore, there has been a downward trend in total dispensed MMEq of opioid analgesics prescribed by dentists.Knowledge Transfer Statement: This study will serve to inform dentists and policy makers on the types and dosage of opioid analgesics being prescribed by dentists. The study may prompt dentists to reflect on and adjust their practice of opioid analgesic prescription in view of the current opioid analgesic epidemic.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".