{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:51:40Z","timestamp":1792083100489,"version":"4.4.0"},"reference-count":33,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":47,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["esskajournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J. exp. orthop."],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The objective of this pilot study was to determine clinically relevant phenogroups of professional football players with hamstring injuries using unsupervised cluster analysis based on demographic and injury\u2010related characteristics, and to assess return to play (RTP) duration with respect to the characteristics of these phenogroups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective cohort study analysed 57 professional football players from Ferencv\u00e1rosi Torna Club with magnetic resonance imaging (MRI)\u2010confirmed acute hamstring injuries reported between 2018 and 2024. Three clustering approaches were implemented and compared: k\u2010means clustering, hierarchical clustering using Ward's linkage method, and mixed\u2010type clustering using Gower distance with partitioning around medoids (PAMs). The optimal number of clusters was determined using silhouette analysis. Between\u2010cluster differences in RTP duration and injury characteristics were assessed using Kruskal\u2013Wallis tests for continuous variables and chi\u2010square tests for categorical variables, with post\u2010hoc Bonferroni correction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Mixed\u2010type clustering identified seven phenogroups based on British Athletics Muscle Injury Classification (BAMIC) grade, lesion length, anatomical tear site, affected muscle and injury extensiveness. The average silhouette width was 0.290, indicating weak to moderate cluster separation. Clusters 1, 3, 4, 5 and 6 were characterised by low\u2010 to moderate\u2010grade injury parameters and median RTP durations ranging from 9 to 27 days. Clusters 2 and 7 displayed high\u2010grade injury characteristics with considerably prolonged RTP (medians of 46.5 and 94.5 days, respectively). Significant between\u2010cluster differences were observed for days to RTP, lesion length, BAMIC grade, injury severity and anatomical tear site (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Unsupervised machine learning may help identify clinically relevant hamstring injury phenogroups based on MRI\u2010derived tear characteristics, which may facilitate prognosis and inform decision\u2010making regarding expected RTP duration in professional football players.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence<\/jats:title>\n                    <jats:p>Level IV, retrospective cohort study.<\/jats:p>\n                  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