{"id":"W4410051974","doi":"10.1111/epi.18446","title":"Development and validation of International Classification of Diseases, 9th and 10th Revision, Clinical Modification‐based algorithms to identify adult epilepsy in electronic health records","year":2025,"lang":"en","type":"article","venue":"Epilepsia","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Center for Advancing Translational Sciences; National Institute on Aging","keywords":"Epilepsy; Diagnosis code; Medicine; Algorithm; Predictive value; Electronic health record; Pediatrics; Internal medicine; Health care; Psychiatry; Mathematics; Population","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":[0.001515666,0.0001155747,0.000291029,0.0004085957,0.00006751972,0.00003955613,0.0004130998,0.00006974459,0.000005479772],"category_scores_gemma":[0.0007193091,0.0001202247,0.00003230996,0.0006018278,0.00004488616,0.0001703428,0.0001416446,0.0002123981,0.000002471324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001863248,"about_ca_system_score_gemma":0.0006941245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001529206,"about_ca_topic_score_gemma":0.00004493192,"domain_scores_codex":[0.9975354,0.0003931196,0.001066532,0.0005043406,0.0003015488,0.0001990591],"domain_scores_gemma":[0.9984573,0.0002855897,0.0004338076,0.0004111111,0.0002999566,0.0001122518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000376749,0.0001300226,0.5769929,0.0002041615,0.00001333735,3.297246e-7,0.0004736543,0.0001552569,0.00004200505,0.03059013,0.0005530395,0.3908075],"study_design_scores_gemma":[0.0004318179,0.00009650704,0.8933533,0.0002827262,0.000003643482,6.031064e-7,0.00003624772,0.1012518,0.0001289691,0.0006542861,0.003674567,0.00008550238],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4957849,0.0004688435,0.4911065,0.01149734,0.000434303,0.0005899055,0.00001111375,0.00004518802,0.0000619844],"genre_scores_gemma":[0.9020684,0.0002958299,0.09702476,0.000431631,0.00002428273,0.00003603388,0.00006262984,0.000006511126,0.00004996098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4062835,"threshold_uncertainty_score":0.4902623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04018435686565271,"score_gpt":0.4089142500125934,"score_spread":0.3687298931469407,"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."}}