{"id":"W4411368998","doi":"10.1186/s40708-025-00259-w","title":"Enhancing cerebral infarct classification by automatically extracting relevant fMRI features","year":2025,"lang":"en","type":"article","venue":"Brain Informatics","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Genentech; National Institutes of Health; Northern California Institute for Research and Education; Servier; BioClinica; University of Southern California; Bristol-Myers Squibb; Eli Lilly and Company; Biogen; Eisai; Alzheimer's Association; U.S. Department of Defense","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science","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":[0.0006313961,0.0002087499,0.0001981991,0.0002343665,0.0004809262,0.0003143592,0.0003658457,0.0001624286,0.0001285761],"category_scores_gemma":[0.006548153,0.0001990592,0.000082211,0.0006985712,0.000125791,0.0007142647,0.00007650111,0.0004590738,0.0002422012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000157758,"about_ca_system_score_gemma":0.0001691442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005432062,"about_ca_topic_score_gemma":0.000006925936,"domain_scores_codex":[0.9979537,0.0001500656,0.0008896223,0.0002300799,0.0004073243,0.0003691676],"domain_scores_gemma":[0.9977416,0.00118387,0.0004143943,0.0004609546,0.00007393176,0.0001251879],"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":[0.00004264092,0.00009541041,0.0002328277,0.0003450629,0.00001664455,0.000001699346,0.003329842,0.000048714,0.7524965,0.07181308,0.1243674,0.04721017],"study_design_scores_gemma":[0.001242233,0.00009985978,0.01000115,0.0003085512,0.00003664679,0.000098114,0.006688063,0.1285818,0.6789234,0.004036521,0.1692728,0.0007107831],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4468291,0.00007592657,0.1970308,0.04238293,0.003267593,0.002028119,0.00006301003,0.002253446,0.3060691],"genre_scores_gemma":[0.9769797,0.00001496417,0.003131388,0.0106344,0.00004867204,0.00004185705,0.00001785929,0.00001829396,0.009112879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5301506,"threshold_uncertainty_score":0.81174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01915683864402996,"score_gpt":0.2820656104785522,"score_spread":0.2629087718345222,"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."}}