{"id":"W2552581476","doi":"10.14742/ajet.2759","title":"A Tale of Three Cases: Examining Accuracy, Efficiency, and Process Differences in Diagnosing Virtual Patient Cases","year":2016,"lang":"en","type":"article","venue":"Australasian Journal of Educational Technology","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Perspective (graphical); Computer science; Process (computing); Virtual patient; Measure (data warehouse); Clinical Practice; Artificial intelligence; Machine learning; Human–computer interaction; Cognitive psychology; Data science; Psychology; Medicine; Data mining","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":[0.01664399,0.0004160931,0.0004111118,0.003329462,0.001539957,0.005492837,0.001779707,0.00186402,0.001653241],"category_scores_gemma":[0.1502127,0.00041121,0.0008143791,0.001149035,0.004296807,0.004834688,0.004141435,0.00190334,0.0003898703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002360752,"about_ca_system_score_gemma":0.0009836929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003235373,"about_ca_topic_score_gemma":0.0034572,"domain_scores_codex":[0.9909481,0.005245513,0.0008636256,0.0008232208,0.001618616,0.0005008631],"domain_scores_gemma":[0.8706988,0.09989795,0.01067885,0.007478521,0.007417387,0.003828429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003321595,0.002742648,0.6380079,0.0004152,0.0006310654,0.001929617,0.173918,0.01338223,0.006062625,0.008481557,0.004933055,0.1461746],"study_design_scores_gemma":[0.0003479443,0.005719797,0.7322972,0.0005603742,0.0005572289,0.004974714,0.1449511,0.05319186,0.01415479,0.0297911,0.01292657,0.0005273877],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951323,0.0001253681,0.002731572,0.0004101924,0.00002053583,0.0001081199,0.00004850048,0.00002808682,0.001395299],"genre_scores_gemma":[0.9954398,0.00006248787,0.003972083,0.0001410539,0.000008119285,0.00006234246,0.00005831887,0.00001089401,0.0002450457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01664399,"threshold_uncertainty_score":0.08802289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.036102852176001,"score_gpt":0.2910611092075533,"score_spread":0.2549582570315523,"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."}}