The relationship of prescriber training level to restricted antibiotic prescribing appropriateness at a tertiary academic hospital: a retrospective study
Bibliographic record
Abstract
Abstract Objective: This study aimed to compare appropriateness of restricted antimicrobial prescriptions, as assessed by antimicrobial stewardship program (ASP) prospective audit and feedback (PAF), between those ordered by medical trainees versus staff. Secondary objectives were to determine whether certain timing factors and other independent variables impacted prescription appropriateness. Design: Single center, retrospective cohort study. Setting: The University of Alberta Hospital a 700-bed tertiary care hospital in Edmonton, Canada. Participants: Prescriptions of six health-authority restricted antibiotics subject to ASP PAF between 2018 and 2023. Cases were excluded if prescriber role or prescription dates or times were unavailable. Methods: Data from a local ASP quality improvement database was extracted. Multiple logistic regression analysis was completed with adjusted odds ratios (aOR) reported. Results: A total of 3,687 restricted antibiotic prescriptions subjected to PAF were included in this study, of which 1,163 (31.5%) were assessed as not appropriately prescribed. Prescriptions written by medical trainees did not have higher odds of appropriateness compared to staff (aOR 1.09 [95% CI 0.94–1.28], P = .25). Weekend prescriptions had a reduced odds of being appropriate (aOR 0.71 [0.60–0.84], P < .0001). Through the course of the Coronavirus Disease 2019 (COVID-19) pandemic, appropriateness improved from 56.2% (prepandemic), 71.5% (peri-pandemic) to 76.9% (postpandemic). Conclusions: No differences were noted in restricted antibiotic prescription appropriateness between medical trainees and staff. Weekend prescriptions were less likely to be appropriate. Improved appropriateness over time may be multifactorial, including implementation of ASP preceding the pandemic. Further studies examining timing factors associated with appropriateness are needed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".