{"id":"W1572009591","doi":"10.1007/11527770_51","title":"Clinical Reasoning Learning with Simulated Patients","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Automaton; Cognition; Artificial intelligence; Process (computing); Encoding (memory); Model-based reasoning; Cognitive science; State (computer science); Cognitive model; Machine learning; Knowledge representation and reasoning; Programming language; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00153298,0.000590306,0.0007001469,0.0005745415,0.0004306563,0.0006161265,0.002241075,0.0003825194,0.00002042422],"category_scores_gemma":[0.0002512093,0.0004832917,0.0001614966,0.0004927439,0.0003859386,0.0006506158,0.0009089674,0.002085904,0.0001089916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003245584,"about_ca_system_score_gemma":0.0003487561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000273334,"about_ca_topic_score_gemma":0.00001996628,"domain_scores_codex":[0.9952402,0.0001108656,0.0008251815,0.001807922,0.001217247,0.0007985558],"domain_scores_gemma":[0.9970789,0.0006368289,0.0006074097,0.0009751766,0.0004789485,0.0002227536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001080605,0.00003603256,0.01030566,0.0000166606,0.00002208091,0.00007040492,0.0004974499,0.3931401,0.000002822278,0.0174957,0.000003698796,0.5783986],"study_design_scores_gemma":[0.0008602049,0.001213538,0.003372113,0.002457974,0.00001769756,0.00004462086,4.680539e-7,0.9306229,0.0001051814,0.002669314,0.05712743,0.001508566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001313224,0.0001641181,0.9914981,0.00009154093,0.001417188,0.0003362875,6.831918e-7,0.0002848928,0.004893935],"genre_scores_gemma":[0.6331773,0.00004139046,0.3541983,0.0006999775,0.001748232,0.000004181621,0.000009072903,0.0001037968,0.01001774],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6372998,"threshold_uncertainty_score":0.9997619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01879511262148965,"score_gpt":0.2696518411835635,"score_spread":0.2508567285620739,"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."}}