{"id":"W4210381030","doi":"10.1523/eneuro.0075-21.2022","title":"Personalized Connectome-Based Modeling in Patients with Semi-Acute Phase TBI: Relationship to Acute Neuroimaging and 6 Month Follow-Up","year":2022,"lang":"en","type":"article","venue":"eNeuro","topic":"Traumatic Brain Injury Research","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"National Center for Complementary and Integrative Health; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; James S. McDonnell Foundation; Government of Canada; Berlin Institute of Health; Horizon 2020 Framework Programme; Deutsche Forschungsgemeinschaft; Ontario Neurotrauma Foundation","keywords":"Neuroimaging; Connectome; Traumatic brain injury; Medicine; Neuroscience; Physical medicine and rehabilitation; Psychology; Functional connectivity; 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":[0.0003972636,0.000391609,0.0002852189,0.0004667782,0.0001964911,0.0004668581,0.0003835986,0.0004256341,0.001154515],"category_scores_gemma":[0.003176534,0.0001994011,0.000353688,0.000359649,0.0002532392,0.0004156661,0.0004522202,0.0004192704,0.0001814672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003473427,"about_ca_system_score_gemma":0.0003054814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007557263,"about_ca_topic_score_gemma":0.008115641,"domain_scores_codex":[0.9999048,0.00003769394,0.000006048834,0.00002606512,0.00001106647,0.00001428112],"domain_scores_gemma":[0.999479,0.0002524748,0.000119416,0.00006242999,0.00003820355,0.00004844225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001223872,0.0003297436,0.6194997,0.00006572201,0.0004671676,0.0007779883,0.0006145017,0.3537611,0.003240672,0.0009761897,0.000688964,0.01835438],"study_design_scores_gemma":[0.0000345437,0.0003712707,0.2326269,0.00001583782,0.00009282475,0.0006489176,0.0003752239,0.7616234,0.0008329395,0.002950002,0.0003999177,0.00002825371],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965799,0.00003380798,0.002897224,0.0000879076,0.00000242027,0.00001048696,0.0001942992,0.0000329387,0.0001610325],"genre_scores_gemma":[0.9991461,0.00002384291,0.0005401888,0.000007552864,0.000001748246,0.00001099026,0.0002005968,0.000004029881,0.00006491021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007557263,"threshold_uncertainty_score":0.01502657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07006395262631535,"score_gpt":0.3337560581812238,"score_spread":0.2636921055549084,"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."}}