{"id":"W3202983345","doi":"10.5853/jos.2021.01312","title":"A Bayesian Framework to Optimize Performance of Pre-Hospital Stroke Triage Scales","year":2021,"lang":"en","type":"article","venue":"Journal of Stroke","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canadian Institutes of Health Research; Alberta Innovates; Freiwillige Akademische Gesellschaft; Canadian Cardiovascular Society; University of Calgary; Wellcome Trust; Wellcome","keywords":"Triage; Medicine; Thrombolysis; Stroke (engine); Acute stroke; Medical emergency; Emergency medicine; Emergency department; Nursing; Myocardial infarction; Internal medicine; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004033901,0.000206611,0.0007367422,0.0002434464,0.00004132722,0.0000269013,0.000261634,0.0001324083,0.000342031],"category_scores_gemma":[0.0005803758,0.0001747108,0.0003765003,0.0002665669,0.00006405541,0.0001624546,0.0001629455,0.0005765632,0.0000115575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001317094,"about_ca_system_score_gemma":0.0001828606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002408603,"about_ca_topic_score_gemma":6.475739e-7,"domain_scores_codex":[0.9977144,0.00003925094,0.0008911449,0.0002122454,0.0008197884,0.0003231959],"domain_scores_gemma":[0.9981099,0.0001375355,0.0005308098,0.000433806,0.0004712838,0.0003166267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01499607,0.004853067,0.4597676,0.002177721,0.006928631,0.004376696,0.0058138,0.004710306,0.2323967,0.0005289743,0.1736096,0.08984091],"study_design_scores_gemma":[0.01783117,0.01457542,0.2790627,0.005770274,0.002475902,0.001581794,0.006429418,0.002775651,0.6037211,0.00006572769,0.06457616,0.001134755],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9676381,0.0006356064,0.02200951,0.003141316,0.0006179111,0.0002768328,0.00003503283,0.00001489715,0.005630773],"genre_scores_gemma":[0.8766863,0.0002866266,0.11802,0.0004000966,0.0005953676,0.000005045584,0.000002734685,0.00002814509,0.00397564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3713244,"threshold_uncertainty_score":0.71245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008303561518146119,"score_gpt":0.2613030075547524,"score_spread":0.2529994460366063,"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."}}