P-1690. The relationship of prescriber role and timing of prescribing to antibiotic prescribing appropriateness at a tertiary academic hospital
Bibliographic record
Abstract
Abstract Background There are limited studies examining antibiotic prescription appropriateness that compare trainees versus attending physicians or time of prescription in relation to working hours. Antimicrobial stewardship (AMS) prospective audit and feedback (PAF) was implemented in 2018 at a tertiary care centre targeting six restricted antibiotics (carbapenems, daptomycin, linezolid, and tigecycline). The agent, regimen, and duration were assessed by AMS pharmacists and/or physicians against institutional prescribing guidelines (expert opinion if not available) and recorded prospectively in the AMS quality improvement database. Real time written and verbal feedback were provided to the most responsible physician. Table 1 Baseline prescription demographics. Methods We examined all prescriptions subjected to PAF from 2018 to 2023, with the primary objective to assess prescription appropriateness for medical trainees versus staff physicians. Secondary objectives were to evaluate whether other variables including time of day (working vs off hours), weekday vs weekend, and the COVID-19 pandemic period (Mar 2020-May 2022) impacted appropriateness. Multiple logistic regression was used to determine factors independently associated with optimal prescribing of antibiotics. Institutional ethics approval was obtained. Figure 1 Odds ratios of prescription appropriateness for each independent variable (reference in parentheses). Results Overall, 3685 prescription audits were included in this study. Baseline prescription demographics are shown in Table 1. Of these, 1106 (32%) audited prescriptions were assessed by AMS PAF as not optimally prescribed requiring actionable AMS intervention. Prescriptions written by trainees were not less appropriate compared to staff (OR 1.27 [95%CI 0.97-1.66]). Off hours prescriptions had lower odds of being optimal (OR 0.81 [0.64-1.02], NS). Other secondary outcomes are reported in Figure 1. Prescriptions initiated by infectious disease (ID) had higher odds of being optimal (4.88 [3.68-6.55]). Conclusion In our cohort, we did not find a difference in appropriateness of restricted antibiotic prescriptions between trainees and staff, with a trend towards decline in optimal prescribing during off hours and weekends. ID had higher odds of optimally prescribing. Appropriateness improved over time and the COVID-19 pandemic did not have a negative effect at our center. Disclosures All Authors: No reported disclosures
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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) | 0.000 | 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 | 0.001 |
| 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".