{"id":"W4210261338","doi":"10.1161/svin.121.000157","title":"Ex Vivo Thrombus Magnetic Resonance Imaging Features and Patient Clinical Data Enable Prediction of Acute Ischemic Stroke Cause","year":2022,"lang":"en","type":"article","venue":"Stroke Vascular and Interventional Neurology","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Magnetic resonance imaging; Thrombus; Emergency department; Medicine; Medical imaging; Stroke (engine); Medical physics; Library science; Radiology; Internal medicine; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003793272,0.0008572535,0.0008044603,0.001282282,0.0001914372,0.001617999,0.0003730512,0.0008074224,0.002034517],"category_scores_gemma":[0.01460791,0.0002654456,0.0002989122,0.0005191098,0.0004083657,0.001340225,0.0005268797,0.0006476442,0.0009017238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001958978,"about_ca_system_score_gemma":0.0003114948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005044739,"about_ca_topic_score_gemma":0.0006020049,"domain_scores_codex":[0.998922,0.0005441638,0.000132101,0.0001675232,0.000174001,0.00006029013],"domain_scores_gemma":[0.9941083,0.003991364,0.0007398078,0.0005305974,0.0003780149,0.0002518224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004664141,0.001004935,0.8792151,0.0002437064,0.0004583247,0.0003858009,0.0001666345,0.0105524,0.01669769,0.0006248049,0.00114404,0.08484238],"study_design_scores_gemma":[0.0002083592,0.004352,0.7709013,0.000148627,0.0005243613,0.002121944,0.0002100503,0.1911956,0.02193338,0.004233547,0.004051932,0.0001189334],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9736469,0.002998385,0.01908938,0.0003655191,0.00005407091,0.000142248,0.001323423,0.0001439887,0.002236065],"genre_scores_gemma":[0.991655,0.0006015502,0.006310611,0.00008679792,0.0001238869,0.00003476199,0.0009338288,0.00001724079,0.0002363137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003793272,"threshold_uncertainty_score":0.02006096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02141057456843841,"score_gpt":0.2849654742279149,"score_spread":0.2635548996594765,"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."}}