{"id":"W3186831836","doi":"10.1111/epi.17002","title":"Patient specific prediction of temporal lobe epilepsy surgical outcomes","year":2021,"lang":"en","type":"article","venue":"Epilepsia","topic":"Epilepsy research and treatment","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Horizon 2020 Framework Programme; Ministerio de Ciencia e Innovación; Fundación BBVA","keywords":"Temporal lobe; Epilepsy; Logistic regression; Receiver operating characteristic; Epilepsy surgery; Probabilistic logic; Artificial intelligence; Medicine; Computer science; Statistics; Psychology; Machine learning; Mathematics; Psychiatry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00292507,0.0006943832,0.0004168575,0.001010856,0.0001529138,0.0005598235,0.0003032841,0.0005133345,0.001359772],"category_scores_gemma":[0.01244834,0.0001359686,0.0005307083,0.0004962904,0.0001876548,0.0005857872,0.0005940198,0.0005930258,0.0002912121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005044061,"about_ca_system_score_gemma":0.0006379694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002924307,"about_ca_topic_score_gemma":0.00404175,"domain_scores_codex":[0.9992571,0.000336451,0.0000641062,0.0001627369,0.0001162502,0.00006331005],"domain_scores_gemma":[0.9954523,0.002546597,0.00114533,0.0002030435,0.0004611615,0.0001914783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005841561,0.0001550033,0.9256693,0.00007582346,0.0003493332,0.0000656198,0.00006570377,0.02894606,0.0008955642,0.0001652059,0.0009126852,0.04211552],"study_design_scores_gemma":[0.00006306555,0.0008177896,0.630462,0.00008414752,0.0002376764,0.0003118911,0.000109818,0.3634324,0.001817374,0.001754595,0.0008646034,0.00004464526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9749227,0.0005752967,0.02087627,0.0003240003,0.00002676232,0.00005489899,0.002301543,0.0001222316,0.0007963656],"genre_scores_gemma":[0.9953405,0.0001098914,0.002879778,0.00002320077,0.00001223181,0.00002642457,0.001435745,0.000006510053,0.0001657685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00292507,"threshold_uncertainty_score":0.01546943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02733547018783701,"score_gpt":0.2925406045990017,"score_spread":0.2652051344111647,"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."}}