{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T22:38:54Z","timestamp":1792622334416,"version":"4.5.0"},"reference-count":30,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["bjui-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BJUI Compass"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aim to develop, compare and internally validate machine\u2010learning models for predicting urine\u2010culture positivity in patients who had both urinalysis and culture ordered and to explore descriptive probability strata. Post hoc secondary analyses examined age subgroups, the incremental contribution of text\u2010derived features, simpler comparators and calibration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Patients and Methods<\/jats:title>\n                    <jats:p>Urine culture results are typically unavailable for 24\u201372\u2009h, creating uncertainty during initial assessment, and machine\u2010learning models may help estimate the probability of culture positivity from routinely collected data. This retrospective study included 2530 urine\u2010sample records originating from three university hospitals. Eligibility was based on paired urinalysis and urine culture records rather than symptom\u2010based diagnostic criteria for urinary tract infection (UTI). Thirteen supervised algorithms were evaluated using a stratified 75:25 sample\u2010record split. This constituted internal validation; records were not grouped by patient or centre because stable cross\u2010centre patient, centre and collection\u2010date identifiers were unavailable.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Several gradient\u2010boosting algorithms showed similar discrimination. CatBoost had the numerically highest test\u2010set AUC of 0.858 (95% CI 0.829\u20130.892), but its AUC did not differ significantly from gradient boosting or XGBoost. At the reported operating threshold, sensitivity was 0.587 (95% CI 0.513\u20130.662), specificity 0.930 (0.903\u20130.951), PPV 0.766 (0.693\u20130.833) and NPV 0.851 (0.821\u20130.882). Exploratory probability strata separated records with different observed rates of culture positivity, but their clinical utility and safety were not evaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Machine\u2010learning models discriminated between culture\u2010positive and culture\u2010negative sample records in a sample\u2010level internal validation. Culture positivity is not synonymous with symptomatic or clinically significant UTI. The findings support further evaluation as diagnostic risk\u2010estimation tools after urinalysis results are available. They do not establish clinical UTI or the safety or effectiveness of starting, withholding or delaying antibiotics. Patient\u2010grouped external validation with clinical outcomes is required before clinical use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/bco2.70280","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T12:21:24Z","timestamp":1788870084000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine\u2010learning prediction of urine\u2010culture positivity in a multicentre test\u2010ordered cohort: Model development and internal validation"],"prefix":"10.1002","volume":"7","author":[{"given":"Isaac Samir","family":"Wasfy","sequence":"first","affiliation":[{"name":"Urology Department, Faculty of Medicine Suez Canal University  Ismailia Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3719-4538","authenticated-orcid":false,"given":"Jamal","family":"Ahmad","sequence":"additional","affiliation":[{"name":"College of 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