ArticlePharmacological reports : PR2026
The application of machine learning in the evaluation of urinary tract infections incidence in patients in a Nursing and Treatment Facility.
Article in Pharmacological reports : PR, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUrinary tract infection (UTI) is a serious problem in the healthcare system. It is caused by bacteria from the gastrointestinal tract. The risk factors that impact the UTI incidence include administration of certain drugs (flozins), sex, use of urinary catheter, and diabetes. This is a retrospective study of the records of 76 patients from a Nursing and Treatment Facility at County Hospital in Drezdenko (Poland) aimed to assess the factors that may have an impact on the incidence of UTI.
methodsThe following factors were taken into consideration: dapagliflozin administration (yes/no), diabetes (yes/no), sex (male/female), kidney failure (yes/no), and use of urinary catheter (yes/no). The impact of the above variables on the UTI incidence was estimated using multivariate regression analysis and machine learning, such as logistic regression, artificial neural networks (ANN), and decision trees (recursive partitioning).
resultsAs revealed by the multivariate regression analysis, UTI was significantly affected only by dapagliflozin administration. The machine learning techniques showed greater sensitivity in detecting significant factors - dapagliflozin administration was identified as the most important one. Moreover, the logistic regression analysis also indicated sex (female). In the case of ANN and decision tree, the other significant factors, besides dapagliflozin intake, in the model were the use of a urinary catheter, sex (female), diabetes, and kidney failure (in descending importance). The variables were listed in the same order of descending importance for both the ANN and the decision tree.
conclusionsIn the case of catheterized patients, the administration of flozins should be cautiously approached, as should the catheterization of patients taking flozins. CLINICAL TRIAL NUMBER: Not applicable.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.