Evaluation of different antimicrobial stewardship models at a rehabilitation hospital: An interrupted time series (ITS) study
Bibliographic record
Abstract
Abstract Objective: To evaluate different prospective audit-and-feedback models on antimicrobial prescribing at a rehabilitation hospital. Design: Retrospective interrupted time series (ITS) and qualitative methods. Setting: A 178-bed rehabilitation hospital within an academic health sciences center. Methods: ITS analysis was used to analyze monthly days of therapy (DOT) per 1,000 patient days (PD) and monthly urine cultures ordered per 1,000 PD. We compared 2 sequential intervention periods to the baseline: (1) a period when a dedicated antimicrobial stewardship (AMS) pharmacist performed prospective audit and feedback and provided urine culture education followed by (2) a period when ward pharmacists performing audit and feedback. We conducted an electronic survey with physicians and semistructured interviews with pharmacists, respectively. Results: Audit and feedback conducted by an AMS pharmacist resulted in a 24.3% relative reduction in total DOT per 1,000 PD (incidence rate ratio [IRR], 0.76; 95% confidence interval [CI], 0.58–0.99; P = .04), whereas we detected no difference between ward pharmacist audit and feedback and the baseline (IRR, 1.20; 95% CI, 0.53–2.70; P = .65). We detected no statistically significant change in monthly urine-culture orders between the AMS pharmacist period and the baseline (level coefficient, 0.81; 95% CI, 0.65–1.01; P = .07). Compared to baseline, the ward pharmacist period showed a statistically significant increase in urine-culture ordering over time (slope coefficient, 1.04; 95% CI, 1.01–1.08; P = .02). The barrier most identified by pharmacists was insufficient time. Conclusions: Audit and feedback conducted by an AMS pharmacist in a rehabilitation hospital was associated with decreased antimicrobial use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".