Urinary tract infection in adults: gaps in current guidelines – opinions from an international multidisciplinary panel and relevance to clinical practice
Bibliographic record
Abstract
PURPOSE: Although urinary tract infections (UTIs) are one of the most common infections encountered in clinical practice, many challenges remain with respect to classification and management. The purpose of this report is to discuss key issues in the management of UTIs and identify gaps in current knowledge and guidelines, as well as future research needs. DESIGN: A multidisciplinary panel of 13 experts from 6 European countries and the United States met on April 27, 2024. They discussed predefined key clinical questions, including classification of UTIs, current management guidelines, management of UTIs in men, antimicrobial switching, and post-treatment asymptomatic bacteriuria. RESULTS: The panel agreed that differentiation between complicated and uncomplicated UTIs is crucial to antimicrobial selection and can impact outcomes. In particular, definitions of complicated UTIs (cUTIs) vary widely between guidelines and in the literature. Patients with cUTIs are not a homogeneous group and differences in risk factors and prognosis should be considered. However, a balance must be sought between appropriate antimicrobial treatment and complexity of guidelines, which can hinder their implementation, especially in primary care. Guidelines published by the European Urology Association and the Infectious Diseases Society of America differ in their antimicrobial treatment recommendations for cUTIs, which is important at a time of increasing antimicrobial resistance. In men with UTIs, it has been established that a longer duration of antimicrobial therapy is needed in cases where fever is present. De-escalation from broad- to narrow-spectrum antimicrobials is recommended wherever possible, and is associated with similar outcomes in many patients relative to remaining on broad-spectrum treatment. Post-treatment asymptomatic bacteriuria should not be assessed, and treatment is not recommended. Non-specialist physician education is crucial to achieving better outcomes for patients with UTIs. IMPLICATIONS: Many challenges remain in the management of UTIs in adults, most notably making an accurate classification, which drives antimicrobial treatment selection. A balance between adequacy of management guidelines and their uptake in routine clinical practice is needed to improve outcomes.
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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 | 0.047 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".