ArticleAnnals of medicine2026
Construction and validation of a machine learning model for predicting urine culture positivity in children with suspected urinary tract infections.
Article in Annals of medicine, 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUrinary tract infections (UTIs) are common in pediatrics. This study aimed to develop and validate a machine learning model for predicting urine culture positivity in children with suspected UTIs and to characterize regional pathogen and antimicrobial resistance profiles.
methodsClinical and laboratory data were collected from children with suspected UTIs. Variables were selected
resultsInfants aged 0-12 months accounted for 61.10% of enrolled children. The overall positive rate of urine culture was 42.17%, higher in boys. Gram-negative bacteria dominated the pathogen spectrum, with the two major strains exhibiting high rates of resistance to first- to third-generation cephalosporins and susceptibility to β-lactam/β-lactamase inhibitor combinations and carbapenems. Seven variables were used to construct seven machine learning models, among which the random forest (RF) model yielded the best performance, with an AUC of 0.814 (95% CI: 0.742-0.875), and sensitivity of 0.770 (95% CI: 0.662-0.865). A web-based calculator was established for individualized prediction.
conclusionsWe developed an RF-based online tool using seven routine laboratory parameters, demonstrating favorable predictive performance for pediatric urine culture positivity. Furthermore, our mapped local pathogen and antimicrobial resistance profiles help clinicians initiate rational empirical antibiotic therapy.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.