A global perspective on improving patient care in uncomplicated urinary tract infection: expert consensus and practical guidance
Bibliographic record
Abstract
OBJECTIVES: Uncomplicated urinary tract infections (uUTIs) are a common problem in female patients. Management is mainly based on empirical prescribing, but there are concerns about overtreatment and antimicrobial resistance (AMR), especially in patients with recurrent uUTIs. METHODS: A multidisciplinary panel of experts met to discuss diagnosis, treatment, prevention, guidelines, AMR, clinical trial design and the impact of COVID-19 on clinical practice. RESULTS: Symptoms remain the cornerstone of uUTI diagnosis, and urine culture is necessary only when empirical treatment fails or rapid recurrence of symptoms or AMR is suspected. Specific antimicrobials are first-line therapy (typically nitrofurantoin, fosfomycin, trimethoprim/sulfamethoxazole and pivmecillinam, dependent on availability and local resistance data). Fluoroquinolones are not first-line options for uUTIs primarily due to safety concerns but also rising resistance rates. High-quality data to support most non-antimicrobial approaches are lacking. Local AMR data specific to community-acquired uUTIs are needed, but representative information is difficult to obtain; instead, identification of risk factors for AMR can provide a basis to guide empirical antimicrobial prescribing. The COVID-19 pandemic has impacted the management of uUTIs in some countries and may have long-lasting implications for future models of care. CONCLUSION: Management of uUTIs in female patients can be improved without increasing complexity, including simplified diagnosis and empirical antimicrobial prescribing based on patient characteristics, including a review of recent antimicrobial use and past pathogen resistance profiles, drug availability and guidelines. Current data for non-antimicrobial approaches are limited. The influence of COVID-19 on telehealth could provide an opportunity to enhance patient care in the long term.
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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 | 0.076 | 0.117 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.030 | 0.031 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".