{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T01:40:32Z","timestamp":1767922832658,"version":"3.49.0"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T00:00:00Z","timestamp":1764979200000},"content-version":"vor","delay-in-days":5,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and validate a machine learning model for predicting oligoclonal band (OCB) positivity using routine cerebrospinal fluid (CSF) and serum biochemical markers to improve the diagnostic accuracy and efficiency of assessing intrathecal immunoglobulin G (IgG) synthesis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this retrospective study (n\u2009=\u20091709), an ensemble model was developed using 8 refined CSF and serum parameters. Combining optimized CatBoost, XGBoost, and LightGBM classifiers, the model was trained and evaluated using a 2-phase workflow, including 5-fold cross-validation and validation on independent internal (n\u2009=\u2009342) and external (n\u2009=\u200949) cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The developed ensemble model achieved a receiver operating characteristic\u2013area under the curve (ROC-AUC) of 0.902 on the internal test set, significantly outperforming the conventional IgG index (ROC-AUC, 0.795). At its optimal threshold, the model demonstrated an accuracy of 0.830, with a sensitivity of 0.714 and a specificity of 0.916. On the external validation cohort, it achieved 90% accuracy and 96% sensitivity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A novel machine learning ensemble model accurately predicts OCB positivity using routine laboratory data and demonstrates superior performance compared with the IgG index. This approach represents a significant step in applying artificial intelligence in laboratory medicine, with the potential to enhance diagnostic efficiency. Prospective, multicenter validation is essential for broader clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ajcp\/aqaf119","type":"journal-article","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T11:58:12Z","timestamp":1759492692000},"page":"933-945","source":"Crossref","is-referenced-by-count":0,"title":["A machine learning model for predicting oligoclonal band positivity using routine cerebrospinal fluid and serum biochemical markers"],"prefix":"10.1093","volume":"164","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8388-8807","authenticated-orcid":false,"given":"Hazar","family":"G\u00f6zg\u00f6z","sequence":"first","affiliation":[{"name":"Faculty of Medicine, Department of Clinical Biochemistry, Dokuz Eylul University , Izmir,","place":["Turkey"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0018-1507","authenticated-orcid":false,"given":"O\u011fuzhan","family":"Orhan","sequence":"additional","affiliation":[{"name":"Engineering Faculty, Computer Engineering, Izmir Institute of Technology , Izmir,","place":["Turkey"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3334-1998","authenticated-orcid":false,"given":"Ba\u015fak","family":"Akan Konuk","sequence":"additional","affiliation":[{"name":"Computation, Information and Technology (CIT), Technical University of Munich , Munich,","place":["Germany"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9211-1944","authenticated-orcid":false,"given":"P\u0131nar","family":"Akan","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Department of Clinical Biochemistry, Dokuz Eylul University , 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