221. Comparison of dental antibiotic prescribing between Australia, England, the United States and British Columbia in 2017
Bibliographic record
Abstract
Abstract Background Antibiotic resistance is recognised as a major public health burden. Dentists overprescribe antibiotics and prescribe for unnecessary indications. Tracking and investigating prescribing practices by healthcare professionals provides insights needed to inform targeted antibiotic stewardship interventions. It is unclear how dental antibiotic prescribing patterns differs between countries. The aim of this study was to compare antibiotic prescribing by dentists in Australia, England the United States (US) and British Columbia (BC). Methods This was a cross-sectional study of dispensed dental antibiotic prescriptions between January 1 and December 31, 2017, from Australia, England, US and BC. Dispensed dental antibiotic prescriptions included those from outpatient pharmacies and healthcare settings. Outcome measures included the proportion of dental antibiotic prescriptions by location and prescribing rates by population. Results English dentists prescribed 1.6 times more antibiotics than those in Australia, and dentists in BC and US prescribed around twice more than Australian dentists. (Australia: 33.2 prescriptions/1000population; England: 53.5 prescriptions/1000population; US: 72.6 prescriptions/1000 population; BC: 65.0 prescriptions/1000 population). The types of antibiotics prescribed were similar across all countries, where penicillins were the predominant class prescribed (66.8–80.5% of antibiotic prescriptions). US dentists and dentists in BC prescribed more clindamycin compared to the dentists in other countries. Conclusion Dentists in the US, England and BC prescribed at relatively higher rates than Australian dentists. The findings from this study should initiate an evaluation by dentists of their prescribing practices and responsibilities regarding their contribution towards antibiotic resistance. Further investigations can be aimed at determining country-specific factors that influence dental antibiotic prescription. Disclosures Leanne Teoh, BDSc(Hons) BPharm(Hons), Australian Government Research Training Program Scholarship (Other Financial or Material Support, Scholarship awarded for the PhD candidature)
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".