{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T19:52:26Z","timestamp":1793130746556,"version":"4.5.0"},"reference-count":39,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"vor","delay-in-days":16,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100007707","name":"Istituto Nazionale per l'Assicurazione Contro Gli Infortuni sul Lavoro","doi-asserted-by":"publisher","award":["BRIC\u20102022 ID28\u2010ID30"],"award-info":[{"award-number":["BRIC\u20102022 ID28\u2010ID30"]}],"id":[{"id":"10.13039\/501100007707","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100031478","name":"NextGenerationEU","doi-asserted-by":"publisher","award":["NRRP M6C2\u2010PNRR\u2010MAD\u20102022\u201012376692_VADALA"],"award-info":[{"award-number":["NRRP M6C2\u2010PNRR\u2010MAD\u20102022\u201012376692_VADALA"]}],"id":[{"id":"10.13039\/100031478","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["JOR Spine"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>Cross\u2010sectional retrospective cohort study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Chronic low back pain (cLBP) is a major cause of disability worldwide, significantly affecting return to work (RTW). This study aimed to assess the biopsychosocial factors influencing occupational functioning in workers with cLBP using artificial intelligence (AI) data\u2010driven patient phenotyping.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Data of workers affected by cLBP were collected through a comprehensive assessment of demographic, clinical, and occupational factors. Hierarchical clustering was employed to identify distinct phenotypes based on patient\u2010reported outcome measures (PROMs), including the Oswestry Disability Index (ODI), Visual Analog Scale (VAS), Work Ability Index (WAI), Nordic score, and Patient Health Questionnaire\u20102 (PHQ\u20102). Independent\n                      <jats:italic>t<\/jats:italic>\n                      tests and Mann\u2013Whitney\n                      <jats:italic>U<\/jats:italic>\n                      tests were used for phenotype profiling, distinguishing between continuous and categorical responses, respectively, to assess the most discriminative queries and highlight the most significantly different features (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 304 patients were included in the analysis. The AI\u2010driven phenotyping approach identified two distinct clusters, representing 51% (Cluster 1) and 49% (Cluster 2) of the dataset. Compared to Cluster 1, Cluster 2 exhibited significantly higher absenteeism (17.00 vs. 5.22\u2009days,\n                      <jats:italic>p\u2009&lt;<\/jats:italic>\n                      \u20090.05), lower WAI (33.34\u2009\n                      <jats:italic>\u00b1<\/jats:italic>\n                      \u20096.84 vs. 38.96\u2009\n                      <jats:italic>\u00b1<\/jats:italic>\n                      \u20094.31,\n                      <jats:italic>p\u2009&lt;<\/jats:italic>\n                      \u20090.05), worse pain\u2010related outcomes in terms of higher VAS (5.98\u2009\n                      <jats:italic>\u00b1<\/jats:italic>\n                      \u20092.06 vs. 4.48\u2009\n                      <jats:italic>\u00b1<\/jats:italic>\n                      \u20092.48,\n                      <jats:italic>p\u2009&lt;<\/jats:italic>\n                      \u20090.05) and ODI (33.52\u2009\n                      <jats:italic>\u00b1<\/jats:italic>\n                      \u200916.56 vs. 20.08\u2009\n                      <jats:italic>\u00b1<\/jats:italic>\n                      \u200913.59,\n                      <jats:italic>p\u2009&lt;<\/jats:italic>\n                      \u20090.05), more frequent occupational exposure to manual handling of loads (84% vs. 16%) and higher psychological distress assessed through PHQ\u20102 (70% vs. 30%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our study identified the most relevant PROMs differentiating between cLBP clusters of patients, emphasizing different levels of absenteeism and pain\u2010related outcomes.<\/jats:p>\n                    <jats:p>These findings contributed to unravel the data\u2010driven AI potential in suggesting personalized interventions targeting specific biopsychosocial profiles, which may improve clinical outcomes and occupational functioning in workers with cLBP, ultimately enhancing their overall well\u2010being.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jsp2.70110","type":"journal-article","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T17:09:36Z","timestamp":1758128976000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Exploring the Biopsychosocial Impact of Chronic Low Back Pain in Workers Through Artificial Intelligence\u2010Driven Phenotyping"],"prefix":"10.1002","volume":"8","author":[{"given":"Paolo","family":"Giaccone","sequence":"first","affiliation":[{"name":"Fondazione Policlinico Universitario Campus Bio\u2010Medico  Rome Italy"},{"name":"Research Unit of Intelligent Technology for Health and Wellbeing Universit\u00e0 Campus Bio\u2010Medico di Roma  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisabetta","family":"de 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