Population-based Assessment of Antibiotics Prescribing by Dentists in Manitoba – A Longitudinal Analysis
Bibliographic record
Abstract
Background: Antibiotic surveillance/stewardship programs have become important tools to promote optimal antibiotic use. Dental prescribing of antibiotics is a significant contributor to overall antibiotic use but has received limited assessment and review at the population level. Methods: Antibiotic prescriptions dispensed from 2014-2019 were evaluated in this longitudinal population–based study conducted in Manitoba, Canada. Antibiotic rates were adjusted for population numbers (per 1000 persons). Linear regression was used to assess trends over time for dentists and physicians. Results: Over the study period, 405,124 antibiotic prescriptions written by dentists were dispensed, representing 9.1% of all antibiotic prescriptions. Physician antibiotic prescribing dropped over time while dentist prescribing remained unchanged (60.1 prescriptions/1000 persons). More than a quarter (27.0%) had potentially inappropriate durations longer than a week. Penicillins were most commonly prescribed (amoxicillin (64.1%), penicillin V (15.0%)). While limited prescriptions were written for the broader spectrum amoxicillin/clavulanate (1.9%), there was a modest increase over time of 12.5% per year (p<0.0015). Analysis by region and income showed relatively consistent results except for northern remote regions where higher rates of dental prescribing were seen. Conclusions: Dental prescribing of antibiotics in Manitoba is stable but higher than national averages with some indications of increased use of broad-spectrum antibiotics. This is in contrast to a significant decline of overall antibiotic prescribing by physicians. Practical Implications: Current data suggest that limiting prescription duration, evaluating the need for a prescription, and increasing scrutiny of the need for broad-spectrum antibiotics may improve the overall quality of dental antibiotic prescribing.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".