{"id":"W2607432848","doi":"10.1016/j.neuroimage.2017.03.027","title":"Multi-center machine learning in imaging psychiatry: A meta-model approach","year":2017,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Central European Institute of Technology; Ministry of Health, British Columbia; Ministry of Education, Youth and Science; Ministerstvo Školství, Mládeže a Tělovýchovy","keywords":"Generalizability theory; Support vector machine; Artificial intelligence; Machine learning; Computer science; Raw data; Similarity (geometry); Data sharing; Sample (material); Schizophrenia (object-oriented programming); Sample size determination; Data mining; Image (mathematics); Medicine; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05141965,0.004614434,0.009691305,0.008515489,0.001986055,0.005435229,0.008114862,0.004475555,0.00419575],"category_scores_gemma":[0.05773634,0.002628886,0.02212782,0.006984442,0.001204855,0.005197491,0.003281451,0.00536648,0.001068875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002319551,"about_ca_system_score_gemma":0.002981176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01743992,"about_ca_topic_score_gemma":0.01762231,"domain_scores_codex":[0.9695678,0.02489657,0.00110898,0.003275674,0.0007537428,0.0003971979],"domain_scores_gemma":[0.9155791,0.07525374,0.002244579,0.004333803,0.001796103,0.0007926868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005078236,0.0009435099,0.04011478,0.004099241,0.3827629,0.0007478427,0.0004405656,0.4256862,0.0006254743,0.01072436,0.007908288,0.1208686],"study_design_scores_gemma":[0.0007723357,0.0006029752,0.006160337,0.000972321,0.1030628,0.0003676098,0.0002129657,0.8238815,0.0005079992,0.06007043,0.003162286,0.0002265206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06513463,0.08875798,0.8310719,0.006212412,0.001169442,0.0004508887,0.003474667,0.002202917,0.001525209],"genre_scores_gemma":[0.7719376,0.0147954,0.2030248,0.001666779,0.0009932156,0.00125031,0.00351395,0.0005933131,0.002224609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05141965,"threshold_uncertainty_score":0.2719364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1600922345230976,"score_gpt":0.3899819900185808,"score_spread":0.2298897554954832,"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."}}