Do family medicine residents optimally prescribe antibiotics for common infectious conditions seen in a primary care setting?
Bibliographic record
Abstract
Background: Antimicrobial resistance is a worldwide phenomenon that leads to a significant number of unnecessary deaths and costly hospital admissions. More than 90% of antibiotic use happens in the community and of this, family physicians account for two-thirds of these prescriptions. Our study aims to determine whether family medicine residents are optimally trained in antibiotic prescribing for common infectious conditions seen in a primary care setting. Methods: This study is a secondary analysis of a prior study of antimicrobial stewardship in two urban primary care clinics in central Toronto, Ontario. A total of 1099 adult patient visits were included that involved family medicine resident trainees, seen between 2015 and 2016. The main outcome measures were resident antibiotic prescription rates for each condition and expert-recommended prescribing practices, the rate prescriptions were issued as delayed prescriptions, and the use of first-line recommended narrow-spectrum antibiotics. Results: Compared to expert-recommended prescribing rates, family medicine residents overprescribed for uncomplicated upper respiratory tract infections (URI) (5.0% [95% CI 2.2% to 9.7%] versus 0% expert recommended) and sinusitis (44.2% [95% CI 32.8% to 55.9%] versus 11%-18% expert range), and under prescribed for pneumonia (53.5% [95% CI 37.7% to 68.8%] versus 100% expert range]). Prescribing rates were within expert recommended ranges for pharyngitis (28.6% [95% CI 16.6% to 43.3%]), bronchitis (3.6% [95% CI 0% to 18.4%]), and cystitis (79.4% [95% CI 70.6% to 86.6%]). Conclusions: The antibiotic prescribing practices of family medicine residents during their training programs indicated overprescribing of antibiotics for some common infection presentations. Further study of antibiotic prescribing in primary care training programs across Canada is recommended to determine if future family physicians are learning appropriate antibiotic prescribing practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".