212Skills Learned during Critical Care Prospective Audit and Feedback are Utilized outside of the Stewardship Environment
Bibliographic record
Abstract
Background. Antimicrobial stewardship programs (ASP) are crucial to optimize antimicrobial utilization in critical care. Prospective audit and feedback is the major intervention used by stewardship programs, yet the impact on clinicians' antimicrobial prescribing behaviours outside of the stewardship environment is unknown. We sought to understand if skills learned during prospective audit and feedback are translated to other areas of clinicians' practice. Methods. Antimicrobial stewardship, through biweekly prospective audit and feedback, was initiated in a 14 bed closed medical-surgical ICU at a single site of a multi-site, community hospital. ICU physicians working in the stewardship ICU also worked in another site ICU at the same community hospital where stewardship had not been formally introduced. We compared antimicrobial utilization between the stewardship and non-stewardship ICUs before and after antimicrobial stewardship implementation using time series analysis. Results. Broad-spectrum antimicrobial use and anti-pseudomonal antimicrobial use decreased post ASP implementation in the stewardship ICU as well as the non-stewardship ICU. In the stewardship ICU broad-spectrum antibiotic use and anti-pseudomonal antibiotic use was decreased by 21.2% (p =0.023) and 20.6% (p = 0.017) respectively compared to 29.8% (p = 0.071) and 38.1% (p = 0.025) in the non-stewardship ICU. Conclusion. Prospective audit and feedback has the potential to change antimicrobial prescribing behaviours among ICU clinicians. Skills learned during prospective audit and feedback are translated to practice settings outside of the stewardship environment. Disclosures. All authors: No reported disclosures.
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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 | 0.010 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".