A global perspective on improving patient care in uncomplicated urinary tract infection: expert consensus and practical guidance
Bibliographic record
Abstract
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.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.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.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 | 0.056 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.028 | 0.027 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".