{"id":"W4387267358","doi":"10.1016/j.arrct.2023.100295","title":"Traumatic Brain Injury Rehabilitation Outcome Prediction Using Machine Learning Methods","year":2023,"lang":"en","type":"article","venue":"Archives of Rehabilitation Research and Clinical Translation","topic":"Traumatic Brain Injury Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Medical Rehabilitation Research; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institutes of Health; National Institute on Disability, Independent Living, and Rehabilitation Research; Ontario Neurotrauma Foundation","keywords":"Rehabilitation; Interpretability; Machine learning; Observational study; Medicine; Traumatic brain injury; Physical therapy; Decision tree; Physical medicine and rehabilitation; Artificial intelligence; Computer science; Psychiatry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007917446,0.001237724,0.001058629,0.002740466,0.000328814,0.001360121,0.0009234964,0.0007566272,0.0009843886],"category_scores_gemma":[0.02449448,0.0002714901,0.001158002,0.001354558,0.0003652021,0.0008593344,0.0009135911,0.001238846,0.0003292541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001014852,"about_ca_system_score_gemma":0.001509817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005275961,"about_ca_topic_score_gemma":0.004487931,"domain_scores_codex":[0.996651,0.002231579,0.0001833628,0.000442927,0.0003643828,0.0001267813],"domain_scores_gemma":[0.9870524,0.01034896,0.001233167,0.0004098444,0.0007527886,0.0002028684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001029373,0.001572835,0.3676988,0.0004309362,0.001513234,0.0001769935,0.0001446953,0.4245306,0.0007843961,0.001264923,0.004385002,0.1964681],"study_design_scores_gemma":[0.0000632664,0.0003478697,0.02726077,0.00009953696,0.00008158365,0.00005119841,0.00005384945,0.9687219,0.0003541984,0.002481012,0.0004604951,0.00002421991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8324172,0.003142254,0.1546775,0.003120814,0.000158186,0.0004048148,0.003031667,0.0007693715,0.002278151],"genre_scores_gemma":[0.9626359,0.0004774082,0.03425404,0.0002065201,0.000131154,0.0002058578,0.001774878,0.00001831037,0.0002959846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007917446,"threshold_uncertainty_score":0.04187196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3895623973258533,"score_gpt":0.5824319026276272,"score_spread":0.1928695053017739,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}