{"id":"W3211923696","doi":"10.1002/jcv2.12042","title":"Ensemble classification of autism spectrum disorder using structural magnetic resonance imaging features","year":2021,"lang":"en","type":"article","venue":"JCPP Advances","topic":"Autism Spectrum Disorder Research","field":"Neuroscience","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"FP7 Health; National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; Canadian Institutes of Health Research; Horizon 2020 Framework Programme; National Institutes of Health; Centro de Investigación Biomédica en Red de Salud Mental; King's College London; Ministerio de Ciencia e Innovación; National Institute for Health and Care Research; European Regional Development Fund; U.S. Department of Defense; Sanofi; Alva Foundation; European Commission; Fondation Brain Canada; Fundación Alicia Koplowitz; Autism Speaks; European Federation of Pharmaceutical Industries and Associations; Instituto de Salud Carlos III; Simons Foundation Autism Research Initiative","keywords":"Autism spectrum disorder; Artificial intelligence; Receiver operating characteristic; Magnetic resonance imaging; Convolutional neural network; Machine learning; Functional magnetic resonance imaging; Computer science; Pattern recognition (psychology); Autism; Psychology; Medicine; Neuroscience; Radiology; Developmental psychology","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":[0.00008998637,0.0001961838,0.0002439569,0.0001271941,0.0002612438,0.0000829428,0.0003507436,0.00004749099,0.0002532393],"category_scores_gemma":[0.0002705565,0.0001902699,0.0000787493,0.0008037726,0.0003105114,0.0005739281,0.0001638231,0.0002644093,0.00001690322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007131576,"about_ca_system_score_gemma":0.0001435747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007028799,"about_ca_topic_score_gemma":0.0002006672,"domain_scores_codex":[0.9977648,0.0001737375,0.0003121151,0.0006579515,0.0005677897,0.0005236344],"domain_scores_gemma":[0.9990827,0.0001827073,0.0001548842,0.0004884576,0.00001768307,0.00007350705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005625969,0.00007821387,0.02522345,0.00008009114,0.00000201204,0.00008536116,0.0003382263,0.0002882615,0.7259803,0.1594618,0.00003126942,0.08837478],"study_design_scores_gemma":[0.0008186672,0.00007956492,0.3455089,0.00008886473,0.00001612544,0.0002293697,0.0003233826,0.01404744,0.4540303,0.1749295,0.009460432,0.000467413],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9277138,0.05311563,0.001531344,0.01122909,0.0005106655,0.0003831366,0.00003828398,0.0001276156,0.005350505],"genre_scores_gemma":[0.9962595,0.0007665334,0.001773711,0.0001031308,0.00004199806,0.00001197362,0.000004152251,0.00003262635,0.00100635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3202854,"threshold_uncertainty_score":0.7758981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02357147568226942,"score_gpt":0.3176780705867669,"score_spread":0.2941065949044975,"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."}}