[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120458-en":3,"doc-seo-120458-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120458,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","A Novel Machine Learning Model to Predict Revision ACL Reconstruction Failure in the MARS Cohort","Machine learning methodology is applied to Multicenter ACL Revision Study (MARS) cohort data to generate patient-specific insight for revision anterior cruciate ligament reconstruction (rACLR). The work aims to build a predictive model for rACLR graft failure and identify the features with the strongest association with failure. The study is designed as a cohort investigation (level of evidence 3) using prospective recruitment and predictive modeling to support risk stratification in orthopedic clinical research.","Thomas Jefferson University  \nJefferson Digital Commons  \n\n| Rothman Institute Faculty Papers | Rothman Institute |\n| --- | --- |\n| 11-14-2024\u003Cbr>A Novel Machine Learning Model to Predict Revision ACL Reconstruction Failure in the MARS Cohort\u003Cbr>Follow this and additional works at: [https://jdc.jefferson.edu/rothman_institute](https://jdc.jefferson.edu/rothman_institute)\u003Cbr>KinjPaalrVthveathopedics Commons\u003Cbr>reinauVsvdw how access to this document benefits you\u003Cbr> |  |\n\nJay Moran  \nRecommended Citation  \nasiveavdan, injal; Vasavada, Vrinda; Moran, Jay; Devana, Sai; Lee, Changhee; Hame, Sharon L.; Jazrawi, Laith M.; Sherman, Orrin H.; Huston, Laura J.; Haas, Amanda K.; Allen, Christina R.; Cooper, Daniel E.; enrdeioL, ehomas M.; Spindler, Kurt P.; Stuart, Michael J.; Ned Amendola, Annunziato; Annunziata, Christopher C.; Arciero, Robert A.; Bach, Bernard R.; Baker, Champ L.; Bartolozzi, Arthur R.; Baumgarten, Keith M.; Berg, Jeffrey H.; Bernas, Geoffrey A.; Brockmeier, Stephen F.; Brophy, Robert H.; Bush-Joseph, rnletAp;atflrrVa,dJdiianda; lCaaurtyo, sames L.; Carpenter, James E.; Cole, Brian J.; Cooper, Jonathan M.; Cox, Charles L.; Creighton, R. Alexander; David, Tal S.; Dunn, Warren R.; Flanigan, David C.; Frederick, Robert W.; Ganley, Theodore J.; Gatt, Charles J.; Gecha, Steven R.; Giffin, James Robert; Hannafin, Jo A.; Lindsay Harris, Norman; Hechtman, Keith S.; Hershman, Elliott B.; Hoellrich, Rudolf G.; Johnson, David C.; Johnson, Timothy S.; Jones, Morgan H.; Kaeding, Christopher C.; Kamath, Ganesh V.; Klootwyk, Thomas E.; Levy, Bruce A.; Ma, C. Benjamin; Maiers, G. Peter; Marx, Robert G.; Matava, Matthew J.; Mathien, Gregory M.; McAllister, David R.; McCarty, Eric C.; McCormack, Robert G.; Miller, Bruce S.; Nissen, Carl W.; O'Neill, Daniel F.; Owens, Brett D.; Parker, Richard D.; Purnell, Mark L.; Ramappa, Arun J.; Rauh, Michael A.; Rettig, Arthur C.; Sekiya, Jon K.; Shea, Kevin G.; Slauterbeck, James R.; Smith, Matthew V.; Spang, Jeffrey T.; Svoboda, Steven J.; Taft, Timothy N.; Tenuta, Joachim J.; Tingstad, Edwin M.; Vidal, Armando F.; Viskontas, Darius G.; White, Richard A.; Williams, James S.; Wolcott, Michelle L.; Wolf, Brian R.; Wright, Rick W.; and York, James J., \"A Novel Machine Learning Model to Predict Revision ACL Reconstruction Failure in the MARS Cohort\" (2024) . Rothman Institute Faculty Papers. Paper 275.  \n[https://jdc.jefferson.edu/rothman_institute/275](https://jdc.jefferson.edu/rothman_institute/275)  \nThis Article is brought to you for free and open access by the Jefferson Digital Commons. The Jefferson Digital Commons is a service of Thomas Jefferson University's Center for Teaching and Learning (CTL) . The Commons is a showcase for Jefferson books and journals, peer-reviewed scholarly publications, unique historical collections from the University archives, and teaching tools. The Jefferson Digital Commons allows researchers and interested readers anywhere in the world to learn about and keep up to date with Jefferson scholarship. This article has been accepted for inclusion in Rothman Institute Faculty Papers by an authorized administrator of the Jefferson Digital Commons. For more information, please contact: [JeffersonDigitalCommons@jefferson.edu](JeffersonDigitalCommons@jefferson.edu).  \nAuthors  \nKinjal Vasavada, Vrinda Vasavada, Jay Moran, Sai Devana, Changhee Lee, Sharon L. Hame, Laith M. Jazrawi, Orrin H. Sherman, Laura J. Huston, Amanda K. Haas, Christina R. Allen, Daniel E. Cooper, Thomas M. DeBerardino, Kurt P. Spindler, Michael J. Stuart, Annunziato Ned Amendola, Christopher C. Annunziata, Robert A. Arciero, Bernard R. Bach, Champ L. Baker, Arthur R. Bartolozzi, Keith M. Baumgarten, Jeffrey H. Berg, Geoffrey A. Bernas, Stephen F. Brockmeier, Robert H. Brophy, Charles A. Bush-Joseph, J. Brad Butler V, James L. Carey, James E. Carpenter, Brian J. Cole, Jonathan M. Cooper, Charles L. Cox, R. Alexander Creighton, Tal S. David, Warren R. Dunn, David C. Flanigan, Robert W. 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