{"id":"W2800752473","doi":"10.1017/cem.2018.97","title":"LO35: Improving the precision of emergency physicians diagnosis of stroke and TIA","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Emergency Medicine","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Medicine; Neurology; Emergency department; Stroke (engine); Medical diagnosis; Prospective cohort study; Univariate analysis; Cohort; Vertigo; Multivariate analysis; Weakness; Internal medicine; Pediatrics; Emergency medicine; Surgery","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":[0.01232451,0.001056845,0.001247461,0.00359405,0.0008478243,0.004434293,0.002024456,0.002081157,0.01598035],"category_scores_gemma":[0.05171876,0.0005636312,0.001196337,0.00125785,0.0006345311,0.001958998,0.003551714,0.001668586,0.004680425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002097522,"about_ca_system_score_gemma":0.005367052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02249078,"about_ca_topic_score_gemma":0.03148844,"domain_scores_codex":[0.9909773,0.004293811,0.0008119849,0.001468852,0.001854585,0.0005935036],"domain_scores_gemma":[0.9681253,0.01953776,0.003097828,0.002016095,0.005550964,0.001672029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00899083,0.001110251,0.2422309,0.001248745,0.001159378,0.0003790092,0.000935897,0.003431626,0.003481855,0.003546596,0.16081,0.5726748],"study_design_scores_gemma":[0.008782462,0.005358588,0.6415634,0.002271226,0.003574848,0.002927895,0.001316878,0.09941563,0.009825137,0.01455746,0.2094691,0.0009374411],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6883432,0.02048679,0.06812534,0.05831462,0.003719684,0.002829392,0.03511216,0.02229237,0.1007765],"genre_scores_gemma":[0.8655332,0.001811923,0.09144816,0.0146478,0.002681263,0.000813487,0.01402174,0.0005442513,0.008498245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02249078,"threshold_uncertainty_score":0.06517905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04089028677025962,"score_gpt":0.3090256971480321,"score_spread":0.2681354103777725,"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."}}