{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T19:16:37Z","timestamp":1790622997509,"version":"4.1.0"},"reference-count":50,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Back and Musculoskeletal Rehabilitation"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Rotator cuff tears, a common cause of shoulder pain, often require surgery when conservative treatment fails. Arthroscopic repair is standard, though re-tear rates remain high.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to identify the shoulder function questionnaires and strength tests that are significant factors for determining the necessity of rotator cuff repair and to build an effective machine learning model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study included 183 patients between January 2008 and December 2016. Among them, 89 patients underwent non-surgical treatment for rotator cuff injuries, whereas 94 underwent arthroscopic repair of full-thickness rotator cuff tears. Fifteen features were analyzed in this study. Six machine learning algorithms, including logistic regression, decision tree, random forest, gradient boosting, neural network, and support vector machine, were trained using 5-fold cross-validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The final machine learning model was evaluated in an independent test set (n\u2009=\u200936). The neural network model demonstrated the highest performance (accuracy: 0.824, F1: 0.749). The top six predictive factors for rotator cuff repair were age, Constant\u2013Murley score, American Shoulder and Elbow Surgeons score, University of California-Los Angeles scale score, body mass index, and shoulder abductor strength deficit.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The probability of arthroscopic rotator cuff repair was lower in individuals with a lower abductor strength deficit, higher shoulder questionnaire scores, younger age, and lower body mass index. When patients or clinicians are deciding on arthroscopic rotator cuff repair, shoulder function questionnaires and differences in shoulder abductor strength should be considered. The clinical relevance is that the use of a preoperative shoulder questionnaire alongside objective abductor strength measurements offers practical guidance for shared decision-making on arthroscopic rotator cuff repair.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/10538127251412325","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T18:55:29Z","timestamp":1767639329000},"page":"992-1001","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine learning prediction for identifying individuals requiring rotator cuff repair: Shoulder muscle strength and questionnaires as the related factors"],"prefix":"10.1177","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6960-3593","authenticated-orcid":false,"given":"Jin-seong","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Physical Therapy, Inje University Ilsan Paik Hospital, Goyang-si, Korea"},{"name":"Department of Physical Therapy, College of Health Science, Yonsei University, Wonju-si, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2050-5503","authenticated-orcid":false,"given":"Ui-jae","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5609-0830","authenticated-orcid":false,"given":"Se-haeng","family":"Jo","sequence":"additional","affiliation":[{"name":"Department of Physical Therapy, Inje University Ilsan Paik Hospital, Goyang-si, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2378-0359","authenticated-orcid":false,"given":"Kyu-sung","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, Guri Hospital, College of Medicine, Hanyang University, Guri-si, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9699-768X","authenticated-orcid":false,"given":"Oh-yun","family":"Kwon","sequence":"additional","affiliation":[{"name":"Department of Physical Therapy, College of Health Science, Yonsei University, Wonju-si, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.2174\/1874325001711010154"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1177\/0363546515593954"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arthro.2012.08.022"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00167-016-4276-x"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.3928\/01477447-20200923-02"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.2106\/JBJS.K.00746"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1097\/BLO.0b013e31802fc175"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmr.2022.12.002"},{"key":"e_1_3_2_10_2","first-page":"163","article-title":"American Academy of orthopaedic surgeons clinical practice guideline on: optimizing the management of rotator cuff problems","volume":"94","author":"Pedowitz RA","year":"2012","unstructured":"Pedowitz RA, Yamaguchi K, Ahmad CS, et al. 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