{"id":"W2330017471","doi":"10.1093/neuonc/nou260.4","title":"MS-05 * UTILIZING A VIRTUAL REALITY SIMULATOR, NEUROTOUCH, TO DETERMINE PROFICIENCY PERFORMANCE BENCHMARKS FOR RESECTION OF SIMULATED BRAIN TUMORS","year":2014,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; NeuroRx Research (Canada)","funders":"","keywords":"Psychomotor learning; Virtual reality; Neurosurgery; Metric (unit); Computer science; Medical physics; Resection; Duration (music); Medicine; Simulation; Cognition; Physical medicine and rehabilitation; Human–computer interaction; Surgery; Operations management; 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":[0.001287272,0.0004596029,0.0002090271,0.0004234956,0.0002182787,0.0003069224,0.0003615664,0.0003190658,0.00184706],"category_scores_gemma":[0.003390027,0.0001618668,0.000317901,0.0001448151,0.0002648636,0.0003412404,0.0007128739,0.0002970392,0.0004773083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003614294,"about_ca_system_score_gemma":0.0006898537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001574065,"about_ca_topic_score_gemma":0.003010283,"domain_scores_codex":[0.9995357,0.0001436392,0.00004910209,0.00009008555,0.0001272049,0.00005429241],"domain_scores_gemma":[0.9980928,0.0005562018,0.000343177,0.000163184,0.0004342307,0.0004103681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.009857182,0.008547289,0.6632319,0.0003775321,0.0003150008,0.0003616222,0.003313044,0.03015703,0.1310034,0.0007988388,0.002277938,0.1497592],"study_design_scores_gemma":[0.0002597983,0.06720848,0.7998908,0.00005020126,0.00008536859,0.0007054512,0.001279699,0.05173664,0.07395182,0.0004060464,0.004345044,0.00008061765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974664,0.000008713236,0.001687616,0.00000662144,0.000003047808,0.00008474336,0.0001113868,0.00003799653,0.000593477],"genre_scores_gemma":[0.9920337,0.00001453412,0.00651002,0.0000171188,0.000002572861,0.0002552518,0.0004548598,0.00001049306,0.0007013741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00184706,"threshold_uncertainty_score":0.006807804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05368099055873142,"score_gpt":0.3525221657514387,"score_spread":0.2988411751927073,"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."}}