{"id":"W2919824149","doi":"10.1177/0272989x19832882","title":"Aiding Risk Information learning through Simulated Experience (ARISE): A Comparison of the Communication of Screening Test Information in Explicit and Simulated Experience Formats","year":2019,"lang":"en","type":"article","venue":"Medical Decision Making","topic":"Patient-Provider Communication in Healthcare","field":"Health Professions","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Sinai Health System; Lunenfeld-Tanenbaum Research Institute; University of Toronto","funders":"","keywords":"Risk communication; Test (biology); Computer science; Psychology; Machine learning; Information retrieval; Medicine; Risk analysis (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":[0.006801734,0.0006472348,0.0004428441,0.0004681513,0.0002329523,0.001325638,0.0008299972,0.001068562,0.00593419],"category_scores_gemma":[0.06788258,0.0002425321,0.0004463996,0.0002229593,0.0005501015,0.001619856,0.001719953,0.001007117,0.0005359623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003768726,"about_ca_system_score_gemma":0.00067758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003078012,"about_ca_topic_score_gemma":0.0002906864,"domain_scores_codex":[0.9937251,0.004821029,0.0003425735,0.0003021946,0.0005881017,0.0002209461],"domain_scores_gemma":[0.9397365,0.05221567,0.003183703,0.001929523,0.001097988,0.001836651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"nonrandomized_trial","study_design_scores_codex":[0.03041192,0.03029753,0.05528992,0.005880346,0.000556214,0.0008575,0.0179148,0.01673607,0.02676363,0.00425454,0.002855538,0.8081819],"study_design_scores_gemma":[0.02494719,0.3125627,0.268392,0.008189419,0.003906815,0.005920275,0.02120891,0.1866377,0.07619291,0.03034196,0.05995211,0.001748062],"study_design_candidate":"nonrandomized_trial","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9766838,0.0003827224,0.01631083,0.0004603445,0.00009134268,0.000655264,0.0001100857,0.0002004097,0.005105192],"genre_scores_gemma":[0.9696797,0.0004498855,0.02765159,0.0002930743,0.00006419246,0.0008900847,0.000122351,0.00001630337,0.0008328417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006801734,"threshold_uncertainty_score":0.03597146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1136822592392243,"score_gpt":0.453634357758752,"score_spread":0.3399520985195277,"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."}}