{"id":"W4280545699","doi":"10.3389/fneur.2022.884693","title":"Interpretable Machine Learning Modeling for Ischemic Stroke Outcome Prediction","year":2022,"lang":"en","type":"article","venue":"Frontiers in Neurology","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Receiver operating characteristic; Stroke (engine); Modified Rankin Scale; Interpretability; Neuroimaging; Radiology; Computed tomography angiography; Angiography; Machine learning; Ischemic stroke; Internal medicine; Ischemia; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000341284,0.0001455616,0.0003521399,0.0003187286,0.0001138539,0.000005643077,0.000165848,0.00007034831,0.0001034929],"category_scores_gemma":[0.0001427106,0.0001613129,0.0001028525,0.0001394111,0.00002959974,0.00005292932,0.0002787373,0.0008273856,0.000001562122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001328897,"about_ca_system_score_gemma":0.00002443511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002419724,"about_ca_topic_score_gemma":0.000001383857,"domain_scores_codex":[0.9986126,0.00006867554,0.0003710216,0.0004029073,0.0001816725,0.0003630966],"domain_scores_gemma":[0.999581,0.00003686678,0.00008081175,0.000227528,0.000020904,0.00005284296],"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":[0.002142363,0.0001115368,0.7834907,0.00007400117,0.0001213907,0.00005969558,0.0002571599,0.08772057,0.003092217,0.00001317938,0.1205018,0.002415343],"study_design_scores_gemma":[0.001977854,0.0006543692,0.0003464144,0.000003298102,0.0000756322,0.00005685627,0.0001746853,0.8006276,0.00008136145,0.00001832253,0.1959012,0.00008247197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6685028,0.0006969522,0.315499,0.002737219,0.002969063,0.001122886,0.00008837227,0.0001901663,0.008193514],"genre_scores_gemma":[0.9843134,0.00002948605,0.007380473,0.002292655,0.0001078573,0.0003717724,0.0001385436,0.00004411785,0.005321668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7831443,"threshold_uncertainty_score":0.657815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01471055587510444,"score_gpt":0.2460454903493684,"score_spread":0.231334934474264,"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."}}