Can We Prevent Recurrent UTIs Without Antibiotics, in Both Those Who Do and Do Not Use Catheters? ICI‐RS 2024
Bibliographic record
Abstract
AIMS: Urinary tract infections are one of the most common reasons for antibiotic prescription. The widespread use of antibiotic treatments contributes to the global health problem of antimicrobial resistance development. To slow down the progression of antimicrobial resistance, it is essential that we explore nonantibiotic preventive treatments for this common condition. We aim to report discussions regarding nonantibiotic preventive strategies for recurrent urinary tract infections in both catheterized and non-catheterized patients that took place at the International Consultation on Incontinence-Research Society meeting in Bristol in 2024. METHODS: We undertook a think-tank session during this multidisciplinary meeting specifically designated for discussion regarding both established and emerging nonantibiotic treatments for UTI prevention in both catheterized and non-catheterized patients. This led to the generation of pertinent research questions, which hope to shape future UTI research. RESULTS: We describe the discussions that took place and document the important research questions that were proposed during the International Consultation on Incontinence-Research Society meeting in Bristol in 2024. CONCLUSIONS: There is a range of established UTI preventative strategies for UTI prevention in both catheterized and non-catheterized patients. Emerging UTI prevention treatments have varying levels of evidence to support their use, and in many areas, further research is needed to establish their place in clinical pathways.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".