Managing recurrent urinary tract infections in kidney transplant patients
Bibliographic record
Abstract
INTRODUCTION: Recurrent urinary tract infections (UTI) are a common clinical problem in kidney transplant recipients. Due to the complex urological anatomy derived from the implantation of the kidney graft, the spectrum of the disease and the broad underlying pathophysiological mechanisms. Recurrent UTI worsen the quality of life, decrease the graft survival and increase the costs of kidney transplantation. Areas covered: In this review, we describe the definitions, clinical characteristics, pathophysiological mechanisms and microbiology of recurrent urinary tract infections in kidney transplantations. The actual published literature on the management of recurrent urinary tract infections is based on case series, observational cohorts and very few clinical trials. In this review, the available evidence is compiled to propose evidence-based strategies to manage these complex cases. Expert commentary: The management of recurrent urinary tract infections in kidney transplant patients requires a proper diagnosis of the underlying mechanism. Early identification of structural or functional urological abnormalities, potentially amenable for surgical correction, is crucial for a successful management. The use of antibiotics to prevent recurrent infections should be carefully evaluated to avoid side effects and emergence of antibiotic-resistant microorganisms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".