{"id":"W4367018969","doi":"10.1161/svin.03.suppl_1.207","title":"Abstract Number ‐ 207: Modeling Optimal Patient Transport in a Stroke Network Capable of Remote Telerobotic Endovascular Therapy","year":2023,"lang":"en","type":"article","venue":"Stroke Vascular and Interventional Neurology","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of Calgary","funders":"","keywords":"Thrombolysis; Stroke (engine); Baseline (sea); Medicine; Modified Rankin Scale; Population; Emergency medicine; Catchment area; Occlusion; Medical emergency; Ischemic stroke; Surgery; Cartography; Geography; Internal medicine; Drainage basin; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001220638,0.001235706,0.001043838,0.0009821043,0.0008992073,0.002182682,0.001876297,0.00252462,0.00894891],"category_scores_gemma":[0.003482261,0.001165881,0.001676313,0.0008078349,0.001268437,0.001072096,0.001182074,0.001493406,0.0005888321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005508511,"about_ca_system_score_gemma":0.003934368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1582748,"about_ca_topic_score_gemma":0.06597838,"domain_scores_codex":[0.9992536,0.0002762405,0.0000202579,0.000189234,0.00003992333,0.0002207346],"domain_scores_gemma":[0.9974934,0.001615064,0.0003089275,0.00005078645,0.0002561116,0.000275701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003294323,0.00002752395,0.001363948,0.000007475589,0.000009875997,0.00003094875,0.000009968199,0.997296,0.00003122339,0.0005987331,0.0002076077,0.0003836684],"study_design_scores_gemma":[0.00002368934,0.00002389722,0.0005728977,0.000005207681,0.000008155686,0.00000805046,0.0000305911,0.9986275,0.00001977977,0.0005318989,0.0001435832,0.00000479035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9040146,0.0004012445,0.0684635,0.002550925,0.0001286735,0.0004388188,0.006094058,0.0003291375,0.01757907],"genre_scores_gemma":[0.983294,0.0001832983,0.007655816,0.00009664866,0.0000262837,0.0002954999,0.001227645,0.0000380221,0.007182744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1582748,"threshold_uncertainty_score":0.314707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02079924383586659,"score_gpt":0.2689568498354403,"score_spread":0.2481576059995737,"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."}}