BLADDER score: evaluating a tool to support urinary diagnostic and antibiotic stewardship in hospitalized adults
Bibliographic record
Abstract
OBJECTIVE: Antibiotic overuse for asymptomatic bacteriuria is common in older adults and can lead to harmful outcomes including antimicrobial resistance. Our objective was to evaluate the impact of a simple scoring tool on urine culturing and antibiotic prescribing for adults with presumed urinary tract infections (UTI). DESIGN: Quasi-experimental study using interrupted time series with segmented regression to evaluate urine culturing and urinary antibiotic use and length of stay (LOS), acute care transfers, and mortality 18 months before and 16 months after the intervention. SETTING: 134-bed complex continuing care and rehabilitation hospital in Ontario, Canada. PARTICIPANTS: Nurses, nurse practitioners, physicians, and other healthcare professionals. INTERVENTION: A multifaceted intervention focusing on a 6-item mnemonic scoring tool called the BLADDER score was developed based on existing minimum criteria for prescribing antibiotics in patients with presumed UTI. The BLADDER score was combined with ward- and prescriber-level feedback and education. RESULTS: Before the intervention, the mean rate of urine culturing was 12.47 cultures per 1,000 patient days; after the intervention, the rate was 7.92 cultures per 1,000 patient days (IRR 0.87; 95% CI, 0.67-1.12). Urinary antibiotic use declined after the intervention from a mean of 40.55 DDD per 1,000 patient days before and 25.96 DDD per 1,000 patient days after the intervention (IRR 0.68; 95% CI, 0.59-0.79). There was no change in mean patient LOS, acute care transfers, or mortality. CONCLUSIONS: The BLADDER score may be a safe and effective tool to support improved diagnostic and antimicrobial stewardship to reduce unnecessary treatment for asymptomatic bacteriuria.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".