{"id":"W2969964521","doi":"10.1002/jmri.26904","title":"Reliability of 3D texture analysis: A multicenter MRI study of the brain","year":2019,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Fondation Brain Canada; ALS Society of Canada","keywords":"Intraclass correlation; Reliability (semiconductor); Neuroimaging; Voxel; Nuclear medicine; Computer science; Psychology; Reproducibility; Medicine; Artificial intelligence; Mathematics; Statistics; Neuroscience; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001523392,0.00013551,0.0006871895,0.0002214864,0.00003198806,0.00001433934,0.0003304007,0.00003327824,0.0001847584],"category_scores_gemma":[0.001163641,0.00008001301,0.0003185521,0.0006054834,0.0001465464,0.00009427856,0.00008956652,0.0006792052,0.000001436261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004405175,"about_ca_system_score_gemma":0.0001021436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008963938,"about_ca_topic_score_gemma":0.000004508167,"domain_scores_codex":[0.9976941,0.0002316757,0.0009277645,0.0001909507,0.0007609624,0.0001945192],"domain_scores_gemma":[0.9979027,0.0002792561,0.0007769692,0.0005428733,0.0004104027,0.00008782178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002195486,0.000540909,0.9561083,0.00009818497,0.00009029348,0.00002729502,0.001522428,0.000940024,0.005283686,0.000003165672,0.0006653733,0.03450076],"study_design_scores_gemma":[0.00355816,0.0004874791,0.9424229,0.0004167921,0.0007800064,0.00006744298,0.00113771,0.04645772,0.0001084958,0.00002266305,0.004467362,0.00007331221],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905384,0.003795427,0.0003213325,0.004244956,0.0002452411,0.0003666814,0.000001547174,0.000004327867,0.0004820575],"genre_scores_gemma":[0.9965276,0.00005636098,0.00235037,0.0005177818,0.00008305404,0.00000133221,3.046443e-7,0.00001444615,0.0004487553],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04551769,"threshold_uncertainty_score":0.3262836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004461918603934783,"score_gpt":0.2722354442724372,"score_spread":0.2677735256685024,"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."}}