Clinical Practice Guideline by Infectious Diseases Society of America (IDSA): 2025 Guideline on Management and Treatment of Complicated Urinary Tract Infections: Selection of Antibiotic Therapy for Complicated UTI
Bibliographic record
Abstract
Abstract Background These recommendations address the empiric choice of antibiotics in suspected complicated urinary tract infection (cUTI). Issues to balance include the need for appropriate initial empiric antimicrobial therapy, the patient's severity of illness, and antimicrobial stewardship concerns. Methods The panel's recommendations are based upon evidence derived from systematic literature reviews that focused on comparative benefits and harms of classes of antibiotics approved for cUTI, starting with publications from 2008 onwards, and our review of predictors of resistance in uropathogens included references identified since 2000. These recommendations adhere to a standardized methodology for rating the certainty of evidence and strength of recommendation according to the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach. Results The guidelines panel suggests initially selecting among preferred empiric antibiotic choices rather than alternative options, stratified for the patient's severity of illness and antimicrobial stewardship considerations. When choosing among these antibiotics for a specific patient, the guideline panel suggests a 4-step process to account for changing resistance patterns and individualized patient needs. The steps are: (1) assess the severity of illness (for initial prioritization of antibiotics), (2) consider patient-specific risk factors for resistant uropathogens (for optimization of coverage), (3) evaluate other patient-specific considerations (to reduce the risk of adverse events), and (4) for patients with sepsis, consult a relevant local antibiogram if available (to further improve the likelihood of giving appropriate empiric therapy). Lastly, the guideline panel suggests selecting definitive effective therapy with a targeted spectrum based on the result of the urine culture. Conclusions Many of the classes of antibiotics approved to treat cUTI demonstrate similar efficacy as other classes of antibiotics in randomized controlled trials. Bacterial resistance prevalence is always changing, and new antibiotics will be developed. In this context, the guidelines panel provides recommendations for choosing empiric antibiotic therapy for cUTI that will help clinicians decide among available antibiotic options at the point of care.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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, 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".