{"id":"W4367662574","doi":"10.1109/vrw58643.2023.00040","title":"Streamlining Epilepsy Surgery Planning Rounds with Virtual Reality","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Workflow; Virtual reality; Computer science; Visualization; Epilepsy; Human–computer interaction; Epilepsy surgery; Space (punctuation); Surgical planning; Data visualization; Medicine; Artificial intelligence; Surgery; Neuroscience; Psychology; Database","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":[0.001649416,0.001240264,0.0005144258,0.001427596,0.0006502316,0.001974673,0.001501069,0.0007958023,0.01377594],"category_scores_gemma":[0.006215988,0.0008706055,0.0007519434,0.0004992746,0.0003731323,0.001461522,0.003133614,0.001283459,0.003839936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004889001,"about_ca_system_score_gemma":0.001659652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002396504,"about_ca_topic_score_gemma":0.003739022,"domain_scores_codex":[0.9986575,0.0004422811,0.00009908677,0.0002466727,0.0003895575,0.0001649593],"domain_scores_gemma":[0.9968203,0.001549335,0.0002250952,0.0004744944,0.0004174727,0.0005133009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008728296,0.000471186,0.002907607,0.0003928715,0.00007161588,0.001151416,0.001425336,0.03928112,0.04100051,0.005900684,0.0374756,0.8690493],"study_design_scores_gemma":[0.0008530008,0.00228169,0.008589222,0.0005891031,0.0001739002,0.006640466,0.001151024,0.4661182,0.07322525,0.02053903,0.419141,0.0006982309],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01813448,0.0005220742,0.957128,0.001446496,0.0006372357,0.0005311455,0.0003069204,0.01340465,0.007889049],"genre_scores_gemma":[0.0927834,0.0007300482,0.8991157,0.0005364046,0.0003502472,0.000508301,0.0004107455,0.001246128,0.004319042],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01377594,"threshold_uncertainty_score":0.04608512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0594668883191156,"score_gpt":0.3245114053072383,"score_spread":0.2650445169881227,"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."}}