{"id":"W2399287554","doi":"10.1097/acm.0000000000001234","title":"A Big Data and Learning Analytics Approach to Process-Level Feedback in Cognitive Simulations","year":2016,"lang":"en","type":"article","venue":"Academic Medicine","topic":"Clinical Reasoning and Diagnostic Skills","field":"Medicine","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto; Professional Engineers Ontario","funders":"","keywords":"Computer science; Process (computing); Visualization; Big data; Artificial intelligence; Learning analytics; Metacognition; Cognition; Machine learning; Data science; Analytics; Human–computer interaction; Orientation (vector space); Data mining; Psychology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001045539,0.0001598856,0.000456673,0.0002172721,0.00004920689,0.000003307392,0.0001713737,0.0002054443,0.00005419526],"category_scores_gemma":[0.2025256,0.00009549824,0.00001382926,0.0005309952,0.0002276519,0.00006600098,0.0001486083,0.0007151868,0.00002487122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000335109,"about_ca_system_score_gemma":0.0001307627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000048333,"about_ca_topic_score_gemma":0.000007732865,"domain_scores_codex":[0.9982333,0.00006639958,0.0004991519,0.0005408161,0.0003608633,0.0002994801],"domain_scores_gemma":[0.9862193,0.01283773,0.0001035567,0.0002974129,0.0001308974,0.0004110964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005494042,0.0001322277,0.7443848,0.0001092713,0.0001121288,0.00003553033,0.005616464,0.00007895815,0.0006846306,0.0001531272,0.005889826,0.2422536],"study_design_scores_gemma":[0.0374219,0.002655961,0.8365318,0.05524239,0.001682251,0.0003327969,0.01539802,0.03675904,0.0002345686,0.003330349,0.00930193,0.001108999],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9582347,0.0006316182,0.02250989,0.01372935,0.0001197231,0.0005820576,0.00006217087,0.00006019585,0.004070324],"genre_scores_gemma":[0.9937952,0.0004695372,0.0002595302,0.002288751,0.0006752469,0.00001270516,0.0001531489,0.00002080329,0.002325094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2411446,"threshold_uncertainty_score":0.8041919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2529543061901321,"score_gpt":0.4380322477492504,"score_spread":0.1850779415591183,"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."}}