{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002134151,0.0001114793,0.0002329596,0.0005237649,0.00006105004,0.000029774,0.0004168424,0.0000770492,0.00001570791],"category_scores_gemma":[0.001446803,0.00007661584,0.00002970351,0.0003655662,0.0001715623,0.0003826075,0.00007093466,0.0001761889,0.000001636769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005369125,"about_ca_system_score_gemma":0.0002597998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001739076,"about_ca_topic_score_gemma":0.00001779626,"domain_scores_codex":[0.9988571,0.00004120963,0.0005083451,0.0001870987,0.0002050872,0.0002011695],"domain_scores_gemma":[0.9979243,0.00113286,0.0005545889,0.0001320335,0.0002047623,0.00005147365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001221247,0.0002344268,0.8307944,0.00002748149,0.00002579604,0.000118569,0.001675733,0.00005849536,0.002266758,0.1137759,0.00002029315,0.05098996],"study_design_scores_gemma":[0.001855223,0.01004955,0.8599479,0.008140222,0.00004544349,0.01743227,0.01932967,0.000356735,0.04100389,0.04016855,0.0006491098,0.001021401],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987664,0.0002492174,0.009164084,0.002502591,0.0003027413,0.00007697601,0.000001478511,0.000008815002,0.00003012903],"genre_scores_gemma":[0.9984133,0.00001731892,0.001438933,0.000007062895,0.00005569116,0.000007215858,9.819441e-8,0.000004693168,0.00005568375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07360731,"threshold_uncertainty_score":0.3124303,"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."}}