{"id":"W4399243542","doi":"10.1080/02664763.2024.2360590","title":"Bayesian modeling framework for optimizing pre-hospital stroke triage decisions","year":2024,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; National Institutes of Health","keywords":"Triage; Bayesian probability; Stroke (engine); Computer science; Emergency medicine; Medicine; Artificial intelligence","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":[0.007752547,0.001535413,0.002892217,0.001783766,0.0008845158,0.002557201,0.003057379,0.002804391,0.005182343],"category_scores_gemma":[0.01965236,0.001712667,0.001980285,0.00168788,0.001363152,0.002219031,0.001946796,0.003314136,0.0009491696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003600834,"about_ca_system_score_gemma":0.004144006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04763269,"about_ca_topic_score_gemma":0.03830963,"domain_scores_codex":[0.9959784,0.002597983,0.0001280266,0.0005256087,0.0004449957,0.0003250158],"domain_scores_gemma":[0.9875183,0.01000044,0.0009360245,0.0002252676,0.0009733282,0.0003467098],"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":[0.00006127851,0.00003412852,0.0007948529,0.00003772765,0.0000640524,0.00006432518,0.00008289986,0.9586364,0.0001147526,0.03100771,0.001289586,0.007812347],"study_design_scores_gemma":[0.00001940178,0.00001344799,0.0001609955,0.00001182867,0.00001401341,0.00000764302,0.00001266188,0.9839648,0.00002827059,0.01518465,0.000572329,0.000009889812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02266636,0.0007376275,0.9666641,0.002838693,0.00009988455,0.0001513668,0.0009940726,0.000477547,0.005370355],"genre_scores_gemma":[0.6713574,0.001459896,0.3111113,0.0009691735,0.0003765601,0.0009753274,0.002059566,0.0002597622,0.01143108],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04763269,"threshold_uncertainty_score":0.09471089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02192572105004228,"score_gpt":0.3179319780876974,"score_spread":0.296006257037655,"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."}}