{"id":"W3000104626","doi":"10.1002/ana.25669","title":"Optimizing Patient Selection for Endovascular Treatment in Acute Ischemic Stroke (SELECT): A Prospective, Multicenter Cohort Study of Imaging Selection","year":2020,"lang":"en","type":"article","venue":"Annals of Neurology","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Prospective cohort study; Confidence interval; Odds ratio; Concordance; Perfusion scanning; Radiology; Stroke (engine); Computed tomography angiography; Cohort; Angiography; Neuroimaging; Internal medicine; Perfusion","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":[0.002361262,0.000410083,0.0003078232,0.0005410152,0.0006068893,0.0007031088,0.0003482946,0.0005355431,0.001227371],"category_scores_gemma":[0.004715244,0.0003401779,0.00051769,0.0005877001,0.0003553591,0.0007745263,0.0006145783,0.0005237595,0.0003066825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000240965,"about_ca_system_score_gemma":0.0004272248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001070568,"about_ca_topic_score_gemma":0.001422143,"domain_scores_codex":[0.998887,0.0005060931,0.00009788801,0.0002366712,0.0001298294,0.000142551],"domain_scores_gemma":[0.9976566,0.0003522015,0.001164779,0.0003168442,0.0001866059,0.0003229846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005158813,0.0001201057,0.9982427,0.00000363484,0.00006328215,0.000052895,0.00008285561,0.00002588011,0.0001215149,0.00001484361,0.00008206332,0.0006742702],"study_design_scores_gemma":[0.0001173349,0.001326261,0.9966427,0.000008067174,0.00009105437,0.0004948199,0.0003848502,0.0004680277,0.0001428042,0.00005054323,0.0002634281,0.00001013981],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996662,0.00004065716,0.00006736442,0.00001984636,0.000003062606,0.00001850809,0.0001009336,0.000001129744,0.0000823425],"genre_scores_gemma":[0.9995812,0.00003023436,0.00008757666,0.00003022553,0.00001232581,0.0000182189,0.0001838782,0.000001384562,0.00005506182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002361262,"threshold_uncertainty_score":0.01248771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.022993365040317,"score_gpt":0.292595997938588,"score_spread":0.269602632898271,"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."}}