{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T06:44:30Z","timestamp":1777877070259,"version":"3.51.4"},"reference-count":50,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100016966","name":"bethune charitable foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016966","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Joint Project on Regional HighIncidence Diseases Research of Guangxi Natural Science Foundation","award":["2024GXNSFAA010073"],"award-info":[{"award-number":["2024GXNSFAA010073"]}]},{"name":"Clinical Research Climbing Plan Project of the First Affiliated Hospital of Guangxi Medical University in 2023"},{"name":"Guangxi Zhuang Autonomous Region \u201cFour New\u201d Research and Practice Project","award":["SX202319"],"award-info":[{"award-number":["SX202319"]}]},{"name":"Guangxi Young and Middle aged Teacher\u2019s Basic Ability Promoting Project","award":["2023KY0115"],"award-info":[{"award-number":["2023KY0115"]}]},{"name":"Guangxi Medical and Health Appropriate Technology Development and Promotion Project","award":["S2024017"],"award-info":[{"award-number":["S2024017"]}]},{"name":"The \u201cMedical Excellence Award\u201d Funded by the Creative Research Development Grant from the First Affiliated Hospital of Guangxi Medical University"},{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["NSFC82360422"],"award-info":[{"award-number":["NSFC82360422"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovation Project of Guangxi Graduate Education","award":["JGY2024094"],"award-info":[{"award-number":["JGY2024094"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Therapeutic Advances in Musculoskeletal"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Immune cells are involved in rheumatoid arthritis (RA), but the link between other blood cell indices and the disease activity of RA, along with the underlying mechanisms, is unclear.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>This study aimed to develop an interpretable machine learning model based on blood cell parameters to assess RA disease severity and assist in personalized treatment decisions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>A retrospective case\u2013control study was conducted with blood routine and biochemical detection data from 4401 patients at the First Affiliated Hospital of Guangxi Medical University, spanning from January 1, 2018, to January 1, 2024. The primary outcome was disease severity stratification. Recursive feature elimination was applied to identify key variables, and 10 machine learning algorithms were benchmarked on 55 clinical features with internal validation. Model interpretability was assessed with SHAP, while logistic regression and restricted cubic spline models were used to examine associations between blood cell indices and disease severity. In addition, Mendelian randomization analysis was performed to explore potential causal relationships.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design:<\/jats:title>\n                    <jats:p>This was a retrospective case\u2013control study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Blood cell indices were identified as the primary factors associated with RA severity. In model evaluation, the Random Forest achieved the best performance, with test set AUCs of 0.870 and 0.874. Mendelian randomization supported a causal relationship between blood cell indices and RA risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>These results reinforce the associations between blood cell indices and RA severity. The machine learning model demonstrates good predictive capabilities for RA severity and may assist clinicians in developing personalized treatment strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/1759720x251407065","type":"journal-article","created":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T12:57:53Z","timestamp":1768654673000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Associations of blood cell indices with the severity of rheumatoid arthritis: a retrospective case\u2013control and machine learning modeling study"],"prefix":"10.1177","volume":"18","author":[{"given":"Rongqing","family":"He","sequence":"first","affiliation":[{"name":"The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People\u2019s Republic of 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