Prospective audit and feedback of piperacillin-tazobactam use in a large urban tertiary care hospital
Bibliographic record
Abstract
Background: Prospective audit and feedback has been shown to decrease antimicrobial exposure and costs, while improving patient outcomes. We evaluated the appropriateness of piperacillin-tazobactam orders and the cost avoidance associated with optimization. Methods: Prospective audit and feedback was performed for all adult patients receiving at least two doses of piperacillin-tazobactam in a large tertiary care facility between January 18 and February 10, 2016. When the antimicrobial regimen was assessed to be suboptimal, a recommendation was made to optimize therapy. Cost avoidance was calculated by subtracting the cost of the new regimen from the cost of the original regimen. Results: Piperacillin-tazobactam orders were considered inappropriate 38.5% of the time. Respiratory indications were appropriate in only 52.6% of cases. Intra-abdominal and skin and soft tissue indications were appropriate 82.6% and 70% of the time, respectively. The cost avoidance associated with this study was projected to be Can$28,766 per year. Conclusions: The inappropriate use of piperacillin-tazobactam was high. There would be value in antimicrobial stewardship interventions targeting its use.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".