{"id":"W4405619525","doi":"10.1161/svin.124.001509","title":"Acute Infarct Core Volume Estimation on Noncontrast Computed Tomography With a Deep Learning Algorithm","year":2024,"lang":"en","type":"article","venue":"Stroke Vascular and Interventional Neurology","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Intraclass correlation; Radiology; Computed tomography angiography; Angiography; Magnetic resonance imaging; Perfusion scanning; Stroke (engine); Perfusion; Algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002618758,0.000914645,0.0006601731,0.001758424,0.0002530278,0.0009175293,0.0009177711,0.0007689881,0.0008867321],"category_scores_gemma":[0.009472854,0.0002519361,0.0005677122,0.000802843,0.0004151201,0.0005389916,0.0008789683,0.0006909055,0.0003035985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009215636,"about_ca_system_score_gemma":0.001452256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007672218,"about_ca_topic_score_gemma":0.006574361,"domain_scores_codex":[0.9990907,0.0002772045,0.0001134989,0.0002868097,0.0001407093,0.00009111113],"domain_scores_gemma":[0.9965598,0.001814351,0.0004801542,0.0002662401,0.0007777315,0.0001016319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007708436,0.00058511,0.09642424,0.0001436769,0.0004320323,0.0002125343,0.00008554859,0.4688357,0.007767487,0.00114925,0.00334837,0.4202451],"study_design_scores_gemma":[0.00002041379,0.0000828875,0.005940043,0.00001281282,0.00002502359,0.00005779851,0.000007967249,0.9907668,0.002212989,0.0006344575,0.0002297925,0.000009054987],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5079396,0.0006743512,0.4859417,0.0002899043,0.00005026025,0.0004245583,0.0008010296,0.001630434,0.002248129],"genre_scores_gemma":[0.8897225,0.0001413272,0.1078977,0.0001306851,0.00003348942,0.0002649208,0.001105241,0.00003624553,0.0006678928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007672218,"threshold_uncertainty_score":0.01525515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007059319714097235,"score_gpt":0.2461749242911005,"score_spread":0.2391156045770032,"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."}}