{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T11:32:11Z","timestamp":1786447931510,"version":"build-2736575974"},"reference-count":78,"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"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Neurotrauma Reports"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>\n                    Early prediction of short- and long-term health outcomes remains a significant medical challenge for clinicians providing care for the traumatic brain injury (TBI) population. In the context of an acute trauma care center, accurate and early prediction of patient outcomes from discharge is critical to better plan for services required by patients with TBI. We aimed to identify the heterogeneous longitudinal recovery trajectories of functional cognitive independence during 5 years post-injury and reveal the demographic, clinical, and injury-related factors associated with trajectory membership. Functional Independence Measure for Cognition (FIM-Cog) was evaluated among Traumatic Brain Injury Model Systems participants diagnosed with a TBI between 1987 and 2024 from discharge, 1, 2, and 5 years post-injury. Latent class growth analysis (LCGA) classified 8060 participants into clusters of FIM-Cog trajectories over 5 years post-injury by fitting 1\u20137 model solutions and using maximum probability rule to estimate cluster membership. Backward stepwise multinomial logistic regression identified factors associated with trajectory membership. Statistical significance was determined at\n                    <jats:italic toggle=\"yes\">p<\/jats:italic>\n                    &lt; 0.05, two-tailed. A five-class model solution revealed five clusters of trajectories characterized as high independence (\n                    <jats:italic toggle=\"yes\">n<\/jats:italic>\n                    = 6060, 75.19%), improved independence (\n                    <jats:italic toggle=\"yes\">n<\/jats:italic>\n                    = 1576, 19.55%), delayed dependence (\n                    <jats:italic toggle=\"yes\">n<\/jats:italic>\n                    = 60, 0.74%), delayed independence (\n                    <jats:italic toggle=\"yes\">n<\/jats:italic>\n                    = 284, 3.52%), and low independence (\n                    <jats:italic toggle=\"yes\">n<\/jats:italic>\n                    = 80, 1.00%). Factors associated with delayed dependence included longer acute stays (odds ratio [OR] = 3.133; 95% confidence interval [CI]: 2.39\u20134.11), a greater number of injuries before age 15 (OR = 2.066; 95% CI: 1.05\u20134.06), wider therapeutic windows (OR = 1.060; 95% CI: 1.04\u20131.08), and lesser same-age injuries (OR = 0.314; 95% CI: 0.10\u20130.95). Factors associated with low independence included longer acute stays (OR = 2.583; 95% CI: 1.90\u20133.51), narrower therapeutic windows (OR = 1.035; 95% CI: 1.02\u20131.05), a greater number of moderate-to severe injuries (OR = 2.120; 95% CI: 1.83\u20132.89) and same-age injuries in their lifetime (OR = 8.687; 95% CI: 1.28\u201358.79). Leveraging LCGA revealed five distinct trajectories of functional cognitive independence during 5 years post-injury. Understanding these risk factors during acute care can inform prevention initiatives for optimizing functional cognitive independence during recovery.\n                  <\/jats:p>","DOI":"10.1177\/2689288x261475917","type":"journal-article","created":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T12:11:21Z","timestamp":1786191081000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Distinct Five-Year Latent Class Trajectories and Predictors of Functional Cognitive Independence Following Traumatic Brain Injury: A TBI Model Systems Study"],"prefix":"10.1177","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-3953-0764","authenticated-orcid":false,"given":"Evan D.","family":"Feigel","sequence":"first","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8467-0762","authenticated-orcid":false,"given":"Charlotte B.","family":"Luster","sequence":"additional","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chris W.","family":"Lewis","sequence":"additional","affiliation":[{"name":"Department of Physical Rehabilitation, University of Washington, Seattle, Washington, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brenna","family":"Finegan","sequence":"additional","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael J.","family":"Mastrodicasa","sequence":"additional","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ma\u00eblle-Marie","family":"Troadec","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology Abdul Latif Jameel Clinic for Machine Learning in Health, Boston, Massachusetts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Guo","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology Abdul Latif Jameel Clinic for Machine Learning in Health, Boston, Massachusetts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ben","family":"Olsen","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology Abdul Latif Jameel Clinic for Machine Learning in Health, Boston, Massachusetts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ignacio","family":"Fuentes","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology Abdul Latif Jameel Clinic for Machine Learning in Health, Boston, Massachusetts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph T.","family":"Giacino","sequence":"additional","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA."},{"name":"Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA."},{"name":"Department of Physical Medicine and Rehabilitation, Massachusetts General Hospital, Boston, Massachusetts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Regina","family":"Barzilay","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology Abdul Latif Jameel Clinic for Machine Learning in 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