{"id":"W3214691841","doi":"10.1002/hbm.25705","title":"Structural connectome differences in pediatric mild traumatic brain and orthopedic injury","year":2021,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Traumatic Brain Injury Research","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of British Columbia; Stollery Children's Hospital; Centre Hospitalier Universitaire Sainte-Justine; University of Alberta; Université de Montréal; Children's Hospital of Eastern Ontario; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Alberta Children's Hospital Research Institute; Hotchkiss Brain Institute, University of Calgary","keywords":"Traumatic brain injury; Connectome; Fractional anisotropy; Diffusion MRI; Connectomics; Clustering coefficient; Psychology; Medicine; Physical medicine and rehabilitation; Neuroscience; Psychiatry; Cluster analysis; Magnetic resonance imaging; Artificial intelligence; Functional connectivity; Radiology; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009059118,0.0002950011,0.0006823309,0.0006361586,0.0002510449,0.0001188214,0.000175276,0.0001663994,0.00093562],"category_scores_gemma":[0.001354016,0.0002742679,0.00009863667,0.0008954143,0.0001999517,0.0001660299,0.0001653188,0.000680653,0.00002713611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009500035,"about_ca_system_score_gemma":0.0002045273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004822017,"about_ca_topic_score_gemma":0.0001606465,"domain_scores_codex":[0.9971074,0.0003640805,0.0006856101,0.0006120655,0.0005867202,0.0006440894],"domain_scores_gemma":[0.9979534,0.001092368,0.0001191177,0.0004565614,0.0001014546,0.0002771725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007925859,0.0001456339,0.9531062,0.00264567,0.0001127799,0.0005118964,0.009076845,8.957234e-7,0.01779728,0.00353418,0.005289884,0.007699528],"study_design_scores_gemma":[0.002192032,0.0001816127,0.9919285,0.0003740841,0.00002888207,0.0001413116,0.002129882,0.0004150424,0.00003103244,0.002083393,0.0002007752,0.0002935113],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896035,0.0007453891,0.00004340689,0.007239941,0.0001048215,0.0005577274,0.00001090841,0.0001027455,0.001591618],"genre_scores_gemma":[0.9956872,0.00004252742,0.0005769234,0.00156021,0.0003273683,0.0000461544,0.00004353859,0.00004205656,0.001674018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03882229,"threshold_uncertainty_score":0.9999776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1450090246994601,"score_gpt":0.3605027911680453,"score_spread":0.2154937664685852,"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."}}