{"id":"W2794490671","doi":"10.1093/schbul/sby018.966","title":"S179. PROGNOSTIC UTILITY OF MULTIVARIATE MORPHOMETRY IN SCHIZOPHRENIA","year":2018,"lang":"en","type":"article","venue":"Schizophrenia Bulletin","topic":"Mental Health and Psychiatry","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Multivariate statistics; Univariate; Psychopathology; Multivariate analysis; Psychology; Voxel-based morphometry; Positive and Negative Syndrome Scale; Schizophrenia (object-oriented programming); Magnetic resonance imaging; Voxel; Brain size; Internal medicine; Clinical psychology; Psychosis; Psychiatry; Medicine; Statistics; White matter; Mathematics; Radiology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008850056,0.0004251311,0.0002628746,0.0009723285,0.0003017512,0.0004092578,0.0001736251,0.0002858026,0.00452032],"category_scores_gemma":[0.004294251,0.0001489265,0.0004792705,0.0009291021,0.0004326486,0.0004327453,0.0004460889,0.000374744,0.0003928238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002357617,"about_ca_system_score_gemma":0.0003856417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001704348,"about_ca_topic_score_gemma":0.002460849,"domain_scores_codex":[0.9998226,0.00007365082,0.00001839017,0.00002081524,0.00004296059,0.00002170681],"domain_scores_gemma":[0.9981005,0.0005067251,0.0008676588,0.0001418331,0.0001796729,0.0002037618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009768512,0.00002681616,0.9640861,0.00002679525,0.00008501685,0.0004085353,0.00006081997,0.001124371,0.00343715,0.000360892,0.000567804,0.02883884],"study_design_scores_gemma":[0.00001691917,0.000171292,0.9911985,0.0000101144,0.00004368759,0.0007408452,0.00006327186,0.005761884,0.0006875262,0.0009339015,0.0003554509,0.0000165852],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953803,0.0003136426,0.001702037,0.00029204,0.00002714072,0.00001617492,0.0007063972,0.00006259484,0.001499727],"genre_scores_gemma":[0.9985356,0.00009324698,0.0008800753,0.000009730134,0.00002234494,0.000005034033,0.0002511721,0.00001101311,0.0001915915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00452032,"threshold_uncertainty_score":0.015122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02710459085080988,"score_gpt":0.2635979015312984,"score_spread":0.2364933106804885,"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."}}