{"id":"W3135210337","doi":"10.21037/jtd-20-2197","title":"Developing a virtual reality simulation system for preoperative planning of thoracoscopic thoracic surgery","year":2021,"lang":"en","type":"article","venue":"Journal of Thoracic Disease","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; Toronto General Hospital; University Health Network","funders":"Hokkaido University; Massachusetts Department of Agricultural Resources","keywords":"Medicine; Virtual reality; Surgical planning; Cardiothoracic surgery; Radiology; Surgery; Medical physics; Computer science; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.001162915,0.0001534387,0.0006370715,0.0001921103,0.0001005407,0.00003749704,0.00006192769,0.00007450212,0.00003524241],"category_scores_gemma":[0.002176502,0.0001256737,0.000368316,0.0003595579,0.00003930541,0.0002501181,0.00002702453,0.0001716292,0.000001629542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001569308,"about_ca_system_score_gemma":0.001077905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.649149e-7,"about_ca_topic_score_gemma":3.310811e-7,"domain_scores_codex":[0.9979025,0.0002773047,0.0009531749,0.0001849915,0.0004870339,0.0001950563],"domain_scores_gemma":[0.9961646,0.001494329,0.000660432,0.000174984,0.001167475,0.0003381523],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.03029272,0.0013041,0.3081988,0.01473464,0.00164119,0.003539331,0.003145572,0.4143563,0.001098042,0.002700948,0.0001910928,0.2187974],"study_design_scores_gemma":[0.007656345,0.001125486,0.6065366,0.05267431,0.004821196,0.0004631971,0.01597889,0.2635514,0.04039311,0.001447874,0.003868504,0.001483153],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8891918,0.0008100767,0.1086424,0.0004339161,0.0003571713,0.0002773272,0.00002356764,0.0000283904,0.0002353945],"genre_scores_gemma":[0.9969981,0.00001115244,0.002409009,0.0001315364,0.0002988361,0.000006662456,0.00003941094,0.00002186558,0.00008341407],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2983378,"threshold_uncertainty_score":0.5124826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1209301239112362,"score_gpt":0.4390334847940458,"score_spread":0.3181033608828096,"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."}}