{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000537493,0.0001233016,0.000293161,0.00007425647,0.00009705604,0.00001761632,0.00004738314,0.0002574506,0.0001650339],"category_scores_gemma":[0.000389701,0.0001041395,0.0000925576,0.0001748673,0.0001298911,0.00004437031,0.000002871802,0.00050164,0.000001714877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000247287,"about_ca_system_score_gemma":0.0001018961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001558298,"about_ca_topic_score_gemma":0.00002572642,"domain_scores_codex":[0.9986267,0.0000497743,0.0003821419,0.0002736749,0.0004631201,0.0002045527],"domain_scores_gemma":[0.9986857,0.0005189051,0.00004563395,0.00005828394,0.0000436812,0.0006477875],"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":[0.001763597,0.0006996873,0.0005503976,0.0003365852,0.000215994,0.0002634337,0.006363007,0.01892987,0.00006421882,0.002375106,0.00009559897,0.9683425],"study_design_scores_gemma":[0.0126701,0.001453209,0.00109908,0.0008495562,0.0001282271,0.00006296392,0.003967847,0.9765114,0.0001618682,0.0001676064,0.00269238,0.0002357357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07885129,0.000145732,0.8949482,0.02530348,0.0002604963,0.0002888205,0.00001269343,0.00004080845,0.0001484333],"genre_scores_gemma":[0.9954682,0.0002103147,0.0005083362,0.003647493,0.0001129459,0.000009449227,0.0000146623,0.00001269106,0.00001590864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9681067,"threshold_uncertainty_score":0.4246687,"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."}}