Hospital-wide Rollout of Antimicrobial Stewardship: A Stepped-Wedge Randomized Trial
Bibliographic record
Abstract
A successful day 3 intensive care unit antimicrobial audit-andfeedback program was expanded hospital-wide, with high rates of orders reviewed, suggestions made, and advice accepted, resulting in a reduction in targeted broad-spectrum antibiotic use among those qualifying for the intervention. Our objective was to rigorously evaluate the impact of an antimicrobial stewardship audit-and-feedback intervention, via a stepped-wedge randomized trial. An effective intensive care unit (ICU) audit-and-feedback program was rolled out to 6 non-ICU services in a randomized sequence. The primary outcome was targeted antimicrobial utilization, using a negative binomial regression model to assess the impact of the intervention while accounting for secular and seasonal trends. The intervention was successfully transitioned, with high volumes of orders reviewed, suggestions made, and recommendations accepted. Among patients meeting stewardship review criteria, the intervention was associated with a large reduction in targeted antimicrobial utilization (−21%, P = .004); however, there was no significant change in targeted antibiotic use among all admitted patients (−1.2%, P = .9), and no reductions in overall costs and microbiologic outcomes. An ICU day 3 audit-and-feedback program can be successfully expanded hospital-wide, but broader benefits on non-ICU wards may require interventions earlier in the course of treatment.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".