{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001572223,0.0002207104,0.0003653252,0.0001256322,0.0003004565,0.00008205076,0.0003172908,0.00003380038,0.0000136202],"category_scores_gemma":[0.0001324495,0.0001916308,0.0001794762,0.00007949062,0.0001163416,0.0002346396,0.0001972266,0.0006751551,0.00001451598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002222906,"about_ca_system_score_gemma":0.00003649301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004410266,"about_ca_topic_score_gemma":0.000007559083,"domain_scores_codex":[0.9986832,0.0000333115,0.0002525521,0.0005539924,0.0001628576,0.0003140471],"domain_scores_gemma":[0.9986612,0.00001481656,0.0001684336,0.001013535,0.00003799658,0.0001040471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002065344,0.004458833,0.9160326,0.000319735,0.0001573666,0.0002808784,0.0002169204,0.001088721,0.06125396,0.001883224,0.002783968,0.01131731],"study_design_scores_gemma":[0.003845303,0.00005198748,0.08268324,0.00003832064,0.0003294937,0.0002455329,0.00001229563,0.8968219,0.0004442557,0.0006019211,0.01459418,0.0003316266],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1409084,0.001696962,0.7206069,0.06133965,0.0003974452,0.004110115,0.0001484244,0.002965339,0.06782673],"genre_scores_gemma":[0.7526878,0.00006852448,0.2438771,0.001661591,0.00004603735,0.0001228489,0.00002616698,0.00006739701,0.001442558],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8957331,"threshold_uncertainty_score":0.7814478,"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."}}