High versus low intensity: What is the optimal approach to prospective audit and feedback in an antimicrobial stewardship program?
Bibliographic record
Abstract
BACKGROUND: Antimicrobial stewardship program (ASP) interventions, such as prospective audit and feedback (PAF), have been shown to reduce antimicrobial use and improve patient outcomes. However, the optimal approach to PAF is unknown. OBJECTIVE: We examined the impact of a high-intensity interdisciplinary rounds-based PAF compared to low-intensity PAF on antimicrobial use on internal medicine wards in a 400-bed community hospital. METHODS: Prior to the intervention, ASP pharmacists performed low-intensity PAF with a focus on targeted antibiotics. Recommendations were made directly to the internist for each patient. High-intensity, rounds-based PAF was then introduced sequentially to 5 internal medicine wards. This PAF format included twice-weekly interdisciplinary rounds, with a review of all internal medicine patients receiving any antimicrobial agent. Antibiotic use and clinical outcomes were measured before and after the transition to high-intensity PAF. An interrupted time-series analysis was performed adjusting for seasonal and secular trends. RESULTS: With the transition from low-intensity to high-intensity PAF, a reduction in overall usage was seen from 483 defined daily doses (DDD)/1,000 patient days (PD) during the low-intensity phase to 442 DDD/1,000 PD in the high-intensity phase (difference, -42; 95% confidence interval [CI], -74 to -9). The reduction in usage was more pronounced in the adjusted analysis, in the latter half of the high intensity period, and for targeted agents. There were no differences seen in clinical outcomes in the adjusted analysis. CONCLUSIONS: High-intensity PAF was associated with a reduction in antibiotic use compared to a low-intensity approach without any adverse impact on patient outcomes. A decision to implement high-intensity PAF approach should be weighed against the increased workload required.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".