{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T14:05:01Z","timestamp":1790085901559,"version":"4.0.1"},"reference-count":34,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"vor","delay-in-days":144,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Infectious mononucleosis is clinically difficult to distinguish from bacterial causes of tonsillitis\/pharyngitis. First-line investigations include monospot testing, despite 63% sensitivity in certain cohorts. Therefore, national recommendations include repeating an initial negative monospot within five to seven days. A point-of-care clinical scoring tool could improve clinical outcomes, increase diagnostic accuracy and reduce unnecessary testing. We conducted a retrospective cohort study at Luton and Dunstable University Hospital including patients aged 15\u201324 presenting with sore throat, lymphadenopathy or fever between 01\/01\/2021 and 31\/01\/2024 who underwent monospot testing. Extracted data included demographics, observations, and laboratory results. Patients were randomly split into training (80%) and testing (20%) cohorts. Eleven parameters were used to develop four predictive models; classical multivariate logistic regression, machine-learning logistic regression with LIBLINEAR approximation, machine-learning decision tree classifier, and a simplified clinical risk-stratification model from machine-learning methods. Total of 278 presentations from 264 patients were included. The machine-learning decision tree classifier demonstrated superior performance, achieving 100% sensitivity, 98.0% specificity, and 98.2% diagnostic accuracy using only three parameters: lymphocyte count, neutrophil count, and alanine aminotransferase. The simplified clinical risk-stratification model demonstrated 83.3% sensitivity, 98.0% specificity, and overall 96.4% accuracy. All four models represent potential methods for developing clinical tools to predict monospot positivity. Our risk-stratification model showed significant promise as an easily memorisable, point-of-care clinical scoring tool. Using this, we propose an alternative diagnostic pathway with early counselling and de-escalation of antibiotics in high-risk cases, and reduced testing in low-risk cases; reducing population-level morbidity and epidemiological spread, whilst improving diagnostic accuracy and conserving resources.<\/jats:p>","DOI":"10.1093\/femsmc\/xtag027","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T11:47:15Z","timestamp":1779450435000},"source":"Crossref","is-referenced-by-count":0,"title":["Development of a clinical scoring tool using machine-learning prediction of monospot positivity in suspected infectious mononucleosis"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4562-6627","authenticated-orcid":false,"given":"Dhruv","family":"Patel","sequence":"first","affiliation":[{"name":"ENT Department, Luton and Dunstable University Hospital, Bedfordshire Hospitals NHS Foundation Trust , LU4 0DZ, Luton ,","place":["United 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