{"id":"W2601000485","doi":"10.1038/srep45639","title":"Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Spatial normalization; Corpus callosum; White matter; Autism; Choroid plexus; Medicine; Psychology; Neuroscience; Biology; Magnetic resonance imaging; Anatomy; Developmental psychology; Radiology; Central nervous system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001944799,0.000112013,0.0004891059,0.0006606555,0.0003167377,0.0001271002,0.0001203958,0.00008286064,0.00003857151],"category_scores_gemma":[0.001067731,0.00009607308,0.0001376721,0.0005958572,0.0006555679,0.00008289575,0.0001145891,0.0003109968,0.000003634344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004602868,"about_ca_system_score_gemma":0.0000848801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000155989,"about_ca_topic_score_gemma":0.00007304228,"domain_scores_codex":[0.9980659,0.00004889772,0.0006001661,0.0006402076,0.0003675529,0.0002772776],"domain_scores_gemma":[0.9981529,0.00003996526,0.0002717492,0.001164574,0.00005284606,0.0003179381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002558676,0.0002292881,0.7971947,0.00005043529,0.0003430546,0.004045018,0.002725473,0.0006197594,0.1903984,0.0001867544,0.00226247,0.001919182],"study_design_scores_gemma":[0.0004483272,0.00001888518,0.9419453,0.000071662,0.0004671642,0.0002700919,0.00007105733,0.05416428,0.0007914603,0.0005114092,0.001131559,0.0001087723],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941965,0.0001195966,0.0021797,0.001338101,0.001054108,0.0002465832,0.000001145069,0.00002604671,0.0008382546],"genre_scores_gemma":[0.9926623,0.00001550757,0.00559756,0.00006400461,0.0000106435,0.000006298364,0.00002780679,0.00001315219,0.001602723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1896069,"threshold_uncertainty_score":0.3917747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02209642555063761,"score_gpt":0.3259493775701288,"score_spread":0.3038529520194911,"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."}}