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Here, we used open field behavioral data collected during the first 3 days post partial thoracic SCI to generate an Acute Functional Score (AFS) that defined three subgroups with divergent recovery trajectories. Applying latent class growth analysis and growth mixture modeling to open field and grid walk testing data, we demonstrated 83-92% prediction accuracy for AFS-defined recovery trajectories. The three subgroups differed significantly in treadmill kinematics and histological assessments of lesion size and astrocyte bridging. Applying the recovery trajectory framework to mice receiving saline or biomaterial vehicle injections at 3 days post-SCI revealed robust predictive accuracy while exposing disproportionate injury severity distributions between experimental groups. The approach enables individualized post-SCI recovery characterization that can neutralize procedural bias, minimize animal numbers, and provide a probabilistic basis for evaluating whether interventions enhance or suppress wound repair processes. Our findings establish a foundation for improving preclinical SCI study design and accelerating identification of effective therapies.<\/jats:p>","DOI":"10.64898\/2026.02.28.708735","type":"posted-content","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T15:25:12Z","timestamp":1772551512000},"source":"Crossref","is-referenced-by-count":0,"title":["Predicting recovery trajectories and injury severity following partial crush spinal cord injury in mice"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5004-3935","authenticated-orcid":false,"given":"Kunyu","family":"Li","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Boston University, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0847-9867","authenticated-orcid":false,"given":"Laboni F.","family":"Hassan","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Boston 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