Moving the needle on dental antibiotic overuse in Canada post COVID-19
Bibliographic record
Abstract
Antimicrobial resistance due to over-prescribing in health care, including in dentistry, has been acknowledged as one of the top ten threats to global health by the World Health Organization. Dentistry is responsible for approximately 10% of antibiotics prescribed worldwide and research has shown up to 80% of antibiotics prescribed by dentists may be unnecessary. During the early months of the coronavirus disease 2019 pandemic, when dental offices handled only dental emergencies, it is probable that antibiotics were prescribed more readily and for longer duration to defer treatment for non-urgent cases. These unprecedented times strengthened the realization that strong dental antimicrobial stewardship practises are required in Canada to keep antimicrobial overuse under control. In countries, such as the United Kingdom and Australia, significant work is ongoing in this regard. Canada has made progress in developing tools for antimicrobial stewardship specifically for physicians in community settings, where the vast majority of antibiotics are prescribed, and it is now time to pay attention to antimicrobial stewardship in the field of dental care. Investments in developing a national level dental prescription database, along with monitoring, education and feedback mechanisms, can strongly support moving the needle on dentist-driven antibiotic overuse in Canada.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".