{"id":"W2154052765","doi":"10.1161/strokeaha.115.010564","title":"Value of Computed Tomographic Perfusion–Based Patient Selection for Intra-Arterial Acute Ischemic Stroke Treatment","year":2015,"lang":"en","type":"article","venue":"Stroke","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"WiLAN (Canada)","funders":"Radboud Universitair Medisch Centrum; Universitair Medisch Centrum Groningen; Leids Universitair Medisch Centrum; Medisch Spectrum Twente; Radboud Universiteit; CARIM School for Cardiovascular Diseases, Universiteit Maastricht; Universiteit Leiden; Maastricht Universitair Medisch Centrum; Universiteit Maastricht","keywords":"Medicine; Computed tomographic; Stroke (engine); Perfusion scanning; Computed tomographic angiography; Perfusion; Ischemic stroke; Radiology; Acute stroke; Cardiology; Selection (genetic algorithm); Endovascular treatment; Computed tomography; Internal medicine; Ischemia; Angiography; Aneurysm","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001407166,0.0003051542,0.0005337296,0.0002879274,0.00006157568,0.00001649416,0.0001160032,0.0001375133,0.00004674921],"category_scores_gemma":[0.00004606133,0.0002600383,0.000304631,0.0002312127,0.00008389357,0.00006200856,0.00005417858,0.0001148037,0.00000871526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003259175,"about_ca_system_score_gemma":0.0002328356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007651211,"about_ca_topic_score_gemma":0.000007656176,"domain_scores_codex":[0.9981909,0.00004162252,0.0004851028,0.0004244559,0.0004667966,0.0003910546],"domain_scores_gemma":[0.9987708,0.00005954982,0.0002354147,0.0003769855,0.0003099126,0.0002473855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.007824246,0.002090007,0.03044419,0.0002399519,0.002164931,0.00003066833,0.001185178,0.0004692419,0.8124287,0.0002410924,0.1102831,0.03259867],"study_design_scores_gemma":[0.02658298,0.01361068,0.002992687,0.0001830194,0.002561058,0.00005916581,0.0007345249,0.04078056,0.7933263,0.00001424654,0.1185837,0.0005711686],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827915,0.00008403285,0.01080178,0.000582857,0.00064366,0.00167632,0.0003302532,0.0001309773,0.002958627],"genre_scores_gemma":[0.9781392,0.00001286609,0.01983634,0.0002466089,0.0003459259,0.0002050278,0.0002930077,0.00004317847,0.0008778579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04031131,"threshold_uncertainty_score":0.9999852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01560957343046662,"score_gpt":0.2585481485763788,"score_spread":0.2429385751459122,"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."}}