Patterns of opioid prescribing by dentists in a pediatric population: a retrospective observational study
Bibliographic record
Abstract
Background: Dentists are regular prescribers of opioid analgesic medications; however, there are few published data on their prescribing practices for children. The aim of this study was to assess opioid prescribing practices of dentists for pediatric patients. Methods: We conducted a retrospective study (2011/12 to 2017/18) using administrative health data of opioid prescribing practices of dentists in Nova Scotia for children and adolescents (age < 18 yr). The main variables of interest were opioid “type” and “load” dentists prescribed (number of dispensed prescriptions/yr, days supplied/prescription and dosage/d per prescription in milligrams of morphine equivalents [MME]). Results: Dentists accounted for a mean of 18.3% (standard deviation 1.5%) of all opioid prescribers for the pediatric population annually but were responsible for 59.9% of all opioid prescriptions and 48.6% of total MME dispensed during the 7-year study period. Oral and maxillofacial surgeons were responsible for 80.7% of all dental-related opioids dispensed. Codeine was most frequently prescribed (78.6% of total MME), followed by oxycodone (11.1%). There were significant downward trends over the study period in the total amount of opioid analgesics dispensed (r = −0.903, p < 0.01), primarily due to a reduction in the total amount of codeine dispensed and number of days supplied per prescription (r = −0.837, p < 0.05). Few opioids were dispensed to children less than 12 years. Interpretation: Dentists in Nova Scotia reduced prescriptions of opioids in the pediatric population between 2011/12 and 2017/18, which may indicate that current opioid prescribing principles are influencing dentists’ prescribing habits. Nonetheless, patients and parents should receive appropriate counselling as to the proper use, risks, storage and potential for misuse of opioids when prescribed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".