{"id":"W3013098014","doi":"10.1109/tmrb.2020.2983199","title":"SlicerVR for Medical Intervention Training and Planning in Immersive Virtual Reality","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Robotics and Bionics","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Institute of Biomedical Imaging and Bioengineering; Southeastern Ontario Academic Medical Organization","keywords":"Virtual reality; Visualization; Computer science; Human–computer interaction; Variety (cybernetics); Software; License; Multimedia; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007089688,0.0008719695,0.0005044728,0.0005215521,0.0001815671,0.001054722,0.001455988,0.0007254276,0.02731702],"category_scores_gemma":[0.002237132,0.0006606118,0.001262441,0.0003363097,0.0004038096,0.000738475,0.002232611,0.0009629205,0.005669723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002569562,"about_ca_system_score_gemma":0.0007873969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001306729,"about_ca_topic_score_gemma":0.001980406,"domain_scores_codex":[0.9995878,0.00009492379,0.00003102034,0.00004817445,0.0001963632,0.00004174124],"domain_scores_gemma":[0.9994407,0.0002599982,0.00003261582,0.000111637,0.00009356419,0.00006146209],"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":[0.001172821,0.0002422945,0.001668393,0.00190613,0.0002533446,0.001770325,0.0010506,0.08747033,0.09110131,0.04013463,0.1398942,0.6333356],"study_design_scores_gemma":[0.0005272633,0.0007946033,0.004174373,0.0005829023,0.000196581,0.005408677,0.0002581361,0.4432758,0.09347653,0.03290553,0.4179866,0.0004129151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005783597,0.0008359652,0.9496685,0.0002840176,0.0002295277,0.0003310847,0.002057994,0.02762393,0.01318537],"genre_scores_gemma":[0.1561352,0.001623709,0.8124885,0.0005691601,0.0001486279,0.000954551,0.005778618,0.01045575,0.01184588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02731702,"threshold_uncertainty_score":0.09138453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07755707497090879,"score_gpt":0.3521891219586494,"score_spread":0.2746320469877406,"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."}}