Patterns of dental antibiotic prescribing in 2017: Australia, England, United States, and British Columbia (Canada)
Bibliographic record
Abstract
OBJECTIVE: Our objective was to compare patterns of dental antibiotic prescribing in Australia, England, and North America (United States and British Columbia, Canada). DESIGN: Population-level analysis of antibiotic prescription. SETTING: Outpatient prescribing by dentists in 2017. PARTICIPANTS: Patients receiving an antibiotic dispensed by an outpatient pharmacy. METHODS: Prescription-based rates adjusted by population were compared overall and by antibiotic class. Contingency tables assessed differences in the proportion of antibiotic class by country. RESULTS: In 2017, dentists in the United States had the highest antibiotic prescribing rate per 1,000 population and Australia had the lowest rate. The penicillin class, particularly amoxicillin, was the most frequently prescribed for all countries. The second most common agents prescribed were clindamycin in the United States and British Columbia (Canada) and metronidazole in Australia and England. Broad-spectrum agents, amoxicillin-clavulanic acid, and azithromycin were the highest in Australia and the United States, respectively. CONCLUSION: Extreme differences exist in antibiotics prescribed by dentists in Australia, England, the United States, and British Columbia. The United States had twice the antibiotic prescription rate of Australia and the most frequently prescribed antibiotic in the US was clindamycin. Significant opportunities exist for the global dental community to update their prescribing behavior relating to second-line agents for penicillin allergic patients and to contribute to international efforts addressing antibiotic resistance. Patient safety improvements will result from optimizing dental antibiotic prescribing, especially for antibiotics associated with resistance (broad-spectrum agents) or C. difficile (clindamycin). Dental antibiotic stewardship programs are urgently needed worldwide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".