{"id":"W4281962565","doi":"10.1111/epi.17320","title":"Development and validation of machine learning models for prediction of seizure outcome after pediatric epilepsy surgery","year":2022,"lang":"en","type":"article","venue":"Epilepsia","topic":"Epilepsy research and treatment","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Children's Hospital of Eastern Ontario; Western University; Centre Hospitalier Universitaire Sainte-Justine; Public Health Ontario; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Epilepsy; Epilepsy surgery; Concordance; Medicine; Logistic regression; Cohort; Univariate; Magnetic resonance imaging; Confidence interval; Univariate analysis; Retrospective cohort study; Machine learning; Artificial intelligence; Surgery; Multivariate analysis; Internal medicine; Radiology; Multivariate statistics; Computer science; Psychiatry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00074455,0.0001273933,0.0003828917,0.000294561,0.0001202182,0.000004896445,0.00004038712,0.00004477032,0.0001604119],"category_scores_gemma":[0.0001192069,0.000114567,0.0001142655,0.0002230663,0.0000272032,0.00008310598,0.0000799659,0.0001860469,0.000001458667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009523841,"about_ca_system_score_gemma":0.0001610191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001810673,"about_ca_topic_score_gemma":0.000001562039,"domain_scores_codex":[0.9984879,0.00008953127,0.0005379535,0.0002346275,0.0004318201,0.000218129],"domain_scores_gemma":[0.9991966,0.000240779,0.0001929375,0.0001528553,0.0001093049,0.0001075626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000782045,0.0002702236,0.9940094,0.0005135922,0.00008746096,0.00001377113,0.0006640357,0.0005052641,0.0001694873,0.00007376169,0.0002016473,0.002709253],"study_design_scores_gemma":[0.003507074,0.001331481,0.9720253,0.00005214039,0.0003673142,0.00005714657,0.000378672,0.01279649,0.006094022,0.0003187122,0.002859091,0.0002125093],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962027,0.0008689048,0.001689815,0.0001583184,0.00009112935,0.0006397013,0.0002172503,0.00003052466,0.0001016286],"genre_scores_gemma":[0.9948596,0.000114285,0.003531347,0.00002329798,0.00006053442,0.000415428,0.0005453419,0.00002398826,0.0004262122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02198411,"threshold_uncertainty_score":0.4671907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0549756931504512,"score_gpt":0.2878424248628405,"score_spread":0.2328667317123893,"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."}}