{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003153852,0.0003268007,0.0006173027,0.0004734448,0.0002140042,0.0008317053,0.0003778525,0.000474467,0.001395389],"category_scores_gemma":[0.0216187,0.00009379078,0.0005541167,0.0005382844,0.0003105821,0.0005474843,0.0002942202,0.0007481249,0.0001721082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004210221,"about_ca_system_score_gemma":0.0006689264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004954656,"about_ca_topic_score_gemma":0.0004872344,"domain_scores_codex":[0.9957182,0.0032253,0.000205631,0.0002925752,0.0004527765,0.0001056394],"domain_scores_gemma":[0.986919,0.007179035,0.004051342,0.0005189881,0.000593438,0.0007382233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006700202,0.0004327391,0.9102809,0.0001263375,0.0008478359,0.0001863393,0.00006899789,0.003048832,0.0006976813,0.0004347188,0.001560022,0.07561537],"study_design_scores_gemma":[0.001146826,0.00487002,0.9668208,0.0001606661,0.001049995,0.001200555,0.0001302529,0.01969277,0.001450136,0.00176983,0.001646511,0.00006167944],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895551,0.002594391,0.003014731,0.001904626,0.0001166437,0.0001251981,0.0002670665,0.00003853075,0.002383648],"genre_scores_gemma":[0.9986961,0.0001268774,0.0007755827,0.0001479056,0.00007729949,0.0000330959,0.0001055672,0.000003386257,0.00003428699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003153852,"threshold_uncertainty_score":0.01667935,"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."}}