Dentists’ Prescribing of Analgesics for Children in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVE: Recently, there has been great interest in the use, abuse and over-prescribing of opioid analgesics for children. However, there is a paucity of evidence on patterns of prescribing of both narcotic and non-narcotic analgesics for children by dentists. METHODS: We used a population-wide prescription drug database (PharmaNet) in British Columbia, Canada, to examine prescribing and dispensing of analgesics surrounding dental procedures. We examined all drugs prescribed for children by dentists between 1997 and 2013, as we had access to data on drug doses and days of medication supply. We also examined trends in the use of various narcotic and non-narcotic analgesics and benzodiazepines. RESULTS: In total, 268 691 children were prescribed at least 1 study drug by a dentist. Codeine was the most frequently prescribed: 50% of children received codeine for more than 3 days. Duration of use of codeine was greatest among children ≥12 years, the longest duration of use being 5 days. CONCLUSIONS: Our study reveals that codeine prescription by dentists increased over the 16-year study period. Codeine is prescribed by dentists for 50% of children; prescriptions are for too long a duration to avoid potential morphine accumulation and are not in line with current treatment guidelines.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".