{"id":"W1966628631","doi":"10.1111/ijs.12411","title":"The Stroke Riskometer™ App: Validation of a Data Collection Tool and Stroke Risk Predictor","year":2014,"lang":"en","type":"article","venue":"International Journal of Stroke","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":140,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Edinburgh","keywords":"Medicine; Stroke (engine); Receiver operating characteristic; Framingham Risk Score; Population; Statistic; Confidence interval; Stroke risk; Risk assessment; Physical therapy; Statistics; Internal medicine; Ischemic stroke; Computer science; Environmental health; Disease","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":[0.04062903,0.0009835665,0.0007462448,0.001508115,0.0009458024,0.001287937,0.001255107,0.001255386,0.002073527],"category_scores_gemma":[0.08071046,0.0004687037,0.001019765,0.001328963,0.0009666249,0.00102424,0.001825316,0.001086234,0.002117058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006649481,"about_ca_system_score_gemma":0.004022866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005328661,"about_ca_topic_score_gemma":0.004200209,"domain_scores_codex":[0.9804125,0.009653045,0.003686669,0.001682851,0.004100447,0.0004645252],"domain_scores_gemma":[0.9474947,0.02585691,0.003042906,0.00469996,0.01778547,0.001119963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0203994,0.01004075,0.6546124,0.001969385,0.0009991445,0.0006769366,0.004201987,0.005163332,0.005242395,0.001562776,0.02527768,0.2698538],"study_design_scores_gemma":[0.005003109,0.01981219,0.8884172,0.0009427321,0.001013992,0.001840253,0.001636517,0.03227881,0.01141251,0.001397505,0.03600743,0.0002377423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8974885,0.0004351743,0.04174249,0.0007416371,0.0003119911,0.03147996,0.02130739,0.001119381,0.005373449],"genre_scores_gemma":[0.7971722,0.0005368341,0.1068816,0.0006855992,0.000184683,0.05968146,0.03110924,0.0002499791,0.003498315],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04062903,"threshold_uncertainty_score":0.2148694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0187874526176264,"score_gpt":0.2869633354837864,"score_spread":0.2681758828661601,"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."}}