{"id":"W3214627594","doi":"10.1161/svin.121.000177","title":"Standardized Reporting of Workflow Metrics in Acute Ischemic Stroke Treatment: Why and How?","year":2021,"lang":"en","type":"article","venue":"Stroke Vascular and Interventional Neurology","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Calgary","funders":"","keywords":"Workflow; Interquartile range; Medicine; Consistency (knowledge bases); Stroke (engine); Percentile; Computer science; Medical physics; Internal medicine; Database; Artificial intelligence; Statistics; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0004326362,0.0001936803,0.000680208,0.0003073939,0.00003293192,0.00001986151,0.00005308989,0.0001218033,0.00009906459],"category_scores_gemma":[0.0005096137,0.0001761655,0.0004061952,0.0002340858,0.0001222105,0.00007518798,0.0001827886,0.0002214819,6.426669e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003710036,"about_ca_system_score_gemma":0.00005445772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002381497,"about_ca_topic_score_gemma":0.00002001708,"domain_scores_codex":[0.9981708,0.0001103997,0.0007048504,0.0004741288,0.0002861301,0.0002536894],"domain_scores_gemma":[0.9989783,0.00009280065,0.0004345625,0.0002760059,0.0001255919,0.00009269864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001877504,0.001263427,0.8045505,0.0009293983,0.00727135,0.003960387,0.0002358152,0.00001787444,0.1312779,0.0007359232,0.01177453,0.03610538],"study_design_scores_gemma":[0.04227284,0.008968,0.4049736,0.0007778076,0.004838293,0.004629837,0.000622719,0.002300434,0.1646348,0.0002855307,0.3647262,0.0009699248],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880639,0.004783047,0.002058215,0.002798265,0.0001270144,0.0002479932,0.00008729573,0.00001981423,0.001814447],"genre_scores_gemma":[0.9930716,0.001672555,0.002963704,0.0003645931,0.00005151301,0.00002846139,0.00009785409,0.00002302505,0.001726764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3995769,"threshold_uncertainty_score":0.718382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02058647262737755,"score_gpt":0.2810596317120559,"score_spread":0.2604731590846784,"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."}}