Nosocomial urinary tract infections caused by extended-spectrum beta-lactamase uropathogens: Prevalence, pathogens, risk factors, and strategies for infection control
Bibliographic record
Abstract
INTRODUCTION: Our goal was to investigate the prevalence and antibiogram pattern of extended spectrum beta-lactamase (ESBL) production among uropathogens using isolates from urine samples collected at the Department of Urology in the Sahloul Hospital, Tunisia We also aimed to identify the risk factors for nosocomial urinary tract infections (UTIs) in patients who underwent transurethral resection of the prostate (TURP) and the measures for infection control. METHODS: Laboratory records of a five-year period from January 2004 to December 2008 were submitted for retrospective analysis to determine the incidence of ESBL infections. A total of 276 isolates were collected. A case-control study involving comparisons between two groups of patients who underwent TURP was performed to determine the risk factors for ESBL infection. Group 1, designated case subjects, included 51 patients with nosocomial UTI after TURP. Group 2, designated control subjects, consisted of 58 randomly selected patients who underwent TURP without nosocomial UTI in the same period. Factors suspected to be implicated in the emergence of ESBL infection were compared between the two groups in order to identify risk factors for infection. A univariate regression analysis was performed, followed by a multivariate one. RESULTS: The annual prevalence of ESBL infection ranged from 1.3-2.5%. After performing univariate and multivariate regression analysis, the main risk factors for ESBL infections were identified as: use of antibiotics the year preceding the admission, duration of catheter use, and bladder washout (p=0.012, p=0.019, and p<0.001. CONCLUSIONS: Urologists have to perform a good hemostasis, especially in endoscopic resections, in order to avoid bladder irrigation and bladder washout and to reduce the time of bladder catheterization, which is a strong risk factor of nosocomial UTIs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".