Evidence from an Applied Research Health Question (AHRQ): Physician-prescribed medications to children for oral health issues
Bibliographic record
Abstract
ObjectivesTo understand changes in prescription patterns over time, and support dental and oral health program planning, Toronto Public Health issued an AHRQ request to collect information on physician-prescribed medications following an oral health-related incident. ApproachAn algorithm was created to identify oral health-related incidents among children and youth aged up to 17 years old in Ontario and Toronto during fiscal years 2013 to 2022. The algorithm used provincial data on physician visits, emergency department visits, inpatient hospitalizations, or day surgeries to define ‘dental-related incidents’. Physician-prescribed medications (e.g., chlorhexidine, opioids, benzodiazepines, antibiotics, NSAIDs and others) within seven days of a dental-related incident were recorded using Ontario Drug Benefit Claims and the Narcotics Monitoring System. ResultsOf 522,674 dental-related incidents in Ontario between 1 April 2013 and 31 March 2023, 9.2% had physician-prescribed medications (versus 8.6% of 90,810 dental-related incidents in Toronto). The proportion of incidents with prescriptions increased from 6.4% in 2013 to 10.7% in 2022 in Ontario, and from 5.9% to 10.3% in Toronto. The most prescribed medication was antibiotics, followed by immediate-release combination medications and non-long-acting medications. Conclusions/ImplicationsOver the 10-year period examined, an increasing proportion of dental-related incidents had physician-prescribed medications, which may be attributed to the implementation of Ontario’s pharmacare program (OHIP+) in 2018. Early access to routine and preventative dental care could become more accessible as part of Canada’s new federal dental program. Results from this AHRQ will inform better resource allocation in dental and oral health program planning, potentially reducing avoidable healthcare costs.
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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 | 0.180 | 0.546 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".