{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0152053,0.0001854331,0.0006050719,0.001320755,0.0002962408,0.00003779901,0.0001393188,0.0001951103,0.00006711356],"category_scores_gemma":[0.02469143,0.0001691708,0.0003044398,0.001561849,0.00150312,0.0002847328,0.00006242319,0.001121515,0.0000180046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004663654,"about_ca_system_score_gemma":0.0001652528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00012668,"about_ca_topic_score_gemma":0.00002845946,"domain_scores_codex":[0.9918969,0.004229214,0.001736535,0.000607711,0.001032853,0.0004967853],"domain_scores_gemma":[0.9425597,0.0561882,0.0001762219,0.0004231786,0.0003349128,0.0003178231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001653739,0.0003796515,0.5899048,0.001468469,0.00009127714,0.000001386123,0.00405493,0.00009328919,0.009867392,0.0008410348,0.0000548911,0.3915892],"study_design_scores_gemma":[0.002836108,0.008507915,0.7265792,0.0003694468,0.00005572364,0.000004525601,0.002875757,0.2349342,0.0001196203,0.02279529,0.0007832665,0.0001388544],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9738765,0.0001551684,0.01325517,0.009827407,0.00009977342,0.001845455,0.00004533497,0.0001847617,0.0007104041],"genre_scores_gemma":[0.7738869,0.0001269672,0.2254058,0.00002482707,0.00007623668,0.00007200686,0.0001354924,0.00004019259,0.0002315667],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3914503,"threshold_uncertainty_score":0.983524,"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."}}