{"id":"W3197578102","doi":"10.3389/fneur.2021.729184","title":"Integrative Neuroinformatics for Precision Prognostication and Personalized Therapeutics in Moderate and Severe Traumatic Brain Injury","year":2021,"lang":"en","type":"review","venue":"Frontiers in Neurology","topic":"Traumatic Brain Injury and Neurovascular Disturbances","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; University of Manitoba","funders":"Canadian Institutes of Health Research; National Institutes of Health; Research Manitoba; National Institute of Neurological Disorders and Stroke; Health Sciences Centre Foundation; Familjen Erling-Perssons Stiftelse; Karolinska Institutet; University of Manitoba","keywords":"Traumatic brain injury; Neuroinformatics; Medicine; Neuroimaging; Omics; Personalized medicine; Precision medicine; Modalities; Concussion; Intensive care medicine; Data science; Bioinformatics; Computer science; Pathology; Poison control; Medical emergency; Injury prevention; Psychiatry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001796026,0.0008877757,0.00141405,0.002478929,0.0003046708,0.001768243,0.001018652,0.001454008,0.003163819],"category_scores_gemma":[0.002729665,0.0002375207,0.0009667685,0.002217185,0.000695523,0.001561592,0.001061242,0.002153875,0.001467634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009967274,"about_ca_system_score_gemma":0.002516956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001566516,"about_ca_topic_score_gemma":0.002739508,"domain_scores_codex":[0.9995282,0.0001640996,0.00006371407,0.00006544596,0.0001448052,0.00003373132],"domain_scores_gemma":[0.9986061,0.0009641454,0.00009890796,0.00003963628,0.0002438356,0.0000473645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005659605,0.00005327416,0.0002533391,0.02755863,0.000247916,0.0001793674,0.0001532011,0.0008854251,0.0009557799,0.01512658,0.02362828,0.9309016],"study_design_scores_gemma":[0.00001935759,0.00008720351,0.00125115,0.01708744,0.0003923614,0.0009252728,0.0001497085,0.000480794,0.000642327,0.0161178,0.9628031,0.00004338915],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000155033,0.9957355,0.000908211,0.001124111,0.0002641451,0.00001337173,0.0000602106,0.0000258373,0.001713466],"genre_scores_gemma":[0.001465247,0.9962038,0.001131971,0.0005347005,0.000189275,0.00001611744,0.00007439897,0.000003946112,0.0003804404],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003163819,"threshold_uncertainty_score":0.010584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05568894008213016,"score_gpt":0.3451668599568682,"score_spread":0.289477919874738,"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."}}