A Systematic Review of Antimicrobial Stewardship Interventions to Improve Management of Bacteriuria in Hospitalized Adults
Bibliographic record
Abstract
OBJECTIVE: To determine whether implementation of antimicrobial stewardship (AMS) interventions improve management of bacteriuria in hospitalized adults. DATA SOURCES: EMBASE, MEDLINE, CINAHL, and Clinical Trials Registries via Cochrane CENTRAL were searched from inception through May 2021. Reference lists of included studies were searched, and Scopus was used to retrieve articles that cited included references. STUDY SELECTION AND DATA EXTRACTION: Randomized and nonrandomized trials, controlled before-after studies, interrupted time-series studies, and repeated measures studies evaluating AMS interventions for hospitalized adult inpatients with bacteriuria were included. Risk of bias was assessed independently by 3 team members and compared. Results were summarized descriptively. DATA SYNTHESIS: The search yielded 5509 articles, of which 13 met inclusion criteria. Most common interventions included education (N = 8) and audit and feedback (N = 5) alone or in combination with other interventions. Where assessed, resource and antimicrobial use primarily decreased and appropriateness of antimicrobial use improved; however, impact on guideline adherence was variable. All studies were rated as having unclear or serious risk of bias. This review summarizes and assesses the quality of evidence for AMS interventions to improve the management of bacteriuria. Results provide guidance to both AMS teams and researchers aiming to develop and/or evaluate AMS interventions for management of bacteriuria. CONCLUSIONS: This review demonstrated benefit of AMS interventions on management of bacteriuria. However, most studies had some risk of bias, and an overall effect across studies is unclear due to heterogeneity in outcome measures.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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 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".