{"id":"W4285394286","doi":"10.1177/15910199221113902","title":"Metric based virtual simulation training for endovascular thrombectomy improves interventional neuroradiologists’ simulator performance","year":2022,"lang":"en","type":"article","venue":"Interventional Neuroradiology","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Virtual reality; Medicine; Fluoroscopy; Simulation; Metric (unit); Benchmark (surveying); Simulation training; Medical physics; Performance metric; Computer science; Radiology; Artificial intelligence; 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":[0.0007572753,0.0003628257,0.0001852805,0.0002041257,0.00008863657,0.0003367181,0.0003584411,0.0002843962,0.004336148],"category_scores_gemma":[0.005092469,0.00008847874,0.0001813178,0.00008142563,0.0001558927,0.000255625,0.0006382345,0.0002071767,0.0004931542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002050112,"about_ca_system_score_gemma":0.000273965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006434679,"about_ca_topic_score_gemma":0.0006482805,"domain_scores_codex":[0.9992009,0.0004798533,0.00004184611,0.0000718748,0.0001435696,0.00006205201],"domain_scores_gemma":[0.9990211,0.000417154,0.0001817435,0.00008507592,0.0001114359,0.0001835018],"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":[0.007434081,0.02600499,0.1166675,0.0009900189,0.0003422087,0.0005723234,0.003631285,0.09849872,0.1468217,0.001081728,0.004940874,0.5930147],"study_design_scores_gemma":[0.0007913247,0.05944421,0.7288327,0.0003303959,0.0001918907,0.002371397,0.001430158,0.1418721,0.04291431,0.001164202,0.02044786,0.0002094483],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927807,0.00009564748,0.0044324,0.00005863995,0.00001771676,0.00005704872,0.00003658957,0.00009257545,0.002428671],"genre_scores_gemma":[0.9944686,0.00008303984,0.004539568,0.00002027388,0.000006986553,0.00004260187,0.0001029049,0.000008477304,0.0007277029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004336148,"threshold_uncertainty_score":0.01450586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08305556807790437,"score_gpt":0.3412947179744096,"score_spread":0.2582391498965053,"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."}}