{"id":"W2616938119","doi":"10.1101/141192","title":"Multisite generalizability of schizophrenia diagnosis classification based on functional brain connectivity","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Bishop's University; Institut Universitaire de Gériatrie de Montréal; Centre Hospitalier de l’Université de Montréal; Institut Universitaire en Santé Mentale de Québec","funders":"National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health; Northwestern University; Massachusetts General Hospital; University of Minnesota; U.S. Department of Energy","keywords":"Generalizability theory; Classifier (UML); Functional connectivity; Artificial intelligence; Cognition; Schizophrenia (object-oriented programming); Machine learning; Functional magnetic resonance imaging; Neuroimaging; Psychology; Computer science; Pattern recognition (psychology); Neuroscience; Developmental psychology; 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":[0.008299722,0.0004536521,0.0007841219,0.001819352,0.0004238791,0.0009124164,0.0004848209,0.0006736998,0.001791514],"category_scores_gemma":[0.02826432,0.0002975738,0.0005934403,0.0005751339,0.00116305,0.0008367471,0.001409813,0.0007114456,0.0002880698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000249227,"about_ca_system_score_gemma":0.0001874089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001362914,"about_ca_topic_score_gemma":0.001859512,"domain_scores_codex":[0.9953727,0.0019525,0.0004684349,0.001383332,0.0006202752,0.0002027816],"domain_scores_gemma":[0.9732151,0.01606273,0.002707326,0.005697175,0.001907931,0.0004097763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002031856,0.0001522054,0.9249776,0.0001058412,0.001136351,0.0002499598,0.0008285046,0.004204409,0.03860157,0.0002222739,0.0003383367,0.02715115],"study_design_scores_gemma":[0.00003166402,0.0003635854,0.9820549,0.0000145035,0.0002099829,0.0005549355,0.000325763,0.01012071,0.004903657,0.001219627,0.0001766964,0.00002389317],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926471,0.0001322929,0.006196741,0.00005618465,0.00001551747,0.00003419201,0.0002744309,0.00004771937,0.0005958744],"genre_scores_gemma":[0.9992326,0.00001407478,0.0004899696,0.0000100905,0.000007381364,0.000009897469,0.0001894294,0.00001032244,0.00003609006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008299722,"threshold_uncertainty_score":0.04389364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05658447697784174,"score_gpt":0.2633416809263278,"score_spread":0.2067572039484861,"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."}}