{"id":"W3034010393","doi":"10.1161/str.51.suppl_1.116","title":"Abstract 116: Predicting Symptomatic Intracranial Hemorrhage After Mechanical Thrombectomy: The TAG Score","year":2020,"lang":"en","type":"article","venue":"Stroke","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Thrombolysis; Stroke (engine); Cohort; Internal medicine; Surgery","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":[0.001029238,0.0005730131,0.0005082376,0.001059888,0.0002014255,0.0009632936,0.000413171,0.0003814179,0.001706316],"category_scores_gemma":[0.005101694,0.0001279258,0.0004901156,0.00113703,0.0002957495,0.000446399,0.0004647089,0.0004366623,0.0005758988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002518751,"about_ca_system_score_gemma":0.0003876438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001073683,"about_ca_topic_score_gemma":0.001185865,"domain_scores_codex":[0.9995232,0.0001530423,0.00008999024,0.00005359892,0.0001353112,0.0000449426],"domain_scores_gemma":[0.9978253,0.0004397042,0.001140084,0.0001306368,0.0002359957,0.000228235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003141154,0.00002829331,0.9945077,0.00001811166,0.0001468558,0.0000412494,0.00001145218,0.0001773962,0.0001636107,0.00002003243,0.0002840829,0.004287043],"study_design_scores_gemma":[0.00006424623,0.0002890337,0.9955331,0.00002110028,0.0002115594,0.0006128988,0.00002589045,0.002440179,0.000346609,0.0001555532,0.0002909899,0.000008776205],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972368,0.0003988087,0.0005352325,0.0001598464,0.00002448677,0.0000197315,0.0006525319,0.0000218851,0.0009506787],"genre_scores_gemma":[0.9981489,0.0001197574,0.0005501222,0.0000347866,0.00003850188,0.000009837892,0.0008962455,0.000004672479,0.0001970574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001706316,"threshold_uncertainty_score":0.005708158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01566335065890955,"score_gpt":0.2448877343895682,"score_spread":0.2292243837306586,"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."}}