{"id":"W2973602284","doi":"10.1016/j.biopsych.2019.09.004","title":"Neuroimaging Heterogeneity in Psychosis: Neurobiological Underpinnings and Opportunities for Prognostic and Therapeutic Innovation","year":2019,"lang":"en","type":"review","venue":"Biological Psychiatry","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":70,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; Centre for Addiction and Mental Health; University of Toronto","funders":"National Institute of Mental Health; Medical Research Council; University of Toronto; Canada Foundation for Innovation; Canadian Institutes of Health Research; Centre for Addiction and Mental Health Foundation; Wellcome Trust","keywords":"Neuroimaging; Schizophrenia (object-oriented programming); Psychosis; Psychology; Clinical psychology; Leverage (statistics); Clinical trial; Psychiatry; Medicine; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001810969,0.001004164,0.002545232,0.002320476,0.0002554739,0.001687114,0.001298985,0.001444988,0.001924154],"category_scores_gemma":[0.00304316,0.0003071767,0.0006589988,0.002449709,0.001276879,0.001490315,0.001008964,0.001573433,0.0005155715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001144594,"about_ca_system_score_gemma":0.00246335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002495056,"about_ca_topic_score_gemma":0.004949526,"domain_scores_codex":[0.9996856,0.00006962436,0.00004725649,0.00009866132,0.0000729138,0.00002589615],"domain_scores_gemma":[0.9980291,0.00145414,0.0001854119,0.00003952545,0.0002329824,0.00005896389],"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":[0.0001628432,0.00003845892,0.001201181,0.01624019,0.000358744,0.0003031292,0.00007375037,0.0006494906,0.0009106235,0.007403084,0.009933398,0.9627252],"study_design_scores_gemma":[0.0001339453,0.000347868,0.01861107,0.04109804,0.002455343,0.00820562,0.0006264605,0.001308567,0.001253328,0.05948075,0.8662607,0.0002183431],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001171936,0.9988375,0.0001682372,0.000516174,0.0000607004,0.000001956875,0.00002083868,0.000003634675,0.0002738043],"genre_scores_gemma":[0.001702542,0.9974235,0.0002894285,0.0002159563,0.0002264783,0.000004642615,0.00002727329,0.000001179598,0.0001090184],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002545232,"threshold_uncertainty_score":0.009577453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3982379372298108,"score_gpt":0.3899815044148543,"score_spread":0.008256432814956527,"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."}}