{"id":"W3194609937","doi":"10.1016/j.compbiomed.2021.104770","title":"Utilizing a multilayer perceptron artificial neural network to assess a virtual reality surgical procedure","year":2021,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Montreal Neurological Institute and Hospital; AO Foundation; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Artificial neural network; Virtual reality; Computer science; Artificial intelligence; Machine learning; Perceptron; Task (project management); Simulation; Engineering; Systems engineering","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":[0.0004350843,0.0001432804,0.0004598732,0.00007245142,0.00007912667,0.000006076736,0.00004494447,0.0001644063,0.0001395307],"category_scores_gemma":[0.0001823629,0.0001069035,0.0000420533,0.0003080136,0.000170092,0.00002253794,0.00007628566,0.0003124131,0.000003383017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002876428,"about_ca_system_score_gemma":0.00005063987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001410536,"about_ca_topic_score_gemma":0.00003658996,"domain_scores_codex":[0.9986857,0.0001677493,0.0003322397,0.0003996902,0.00009879467,0.0003158764],"domain_scores_gemma":[0.9990693,0.0004636163,0.00003668503,0.0001338078,0.00005530799,0.0002412479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00392073,0.0005396707,0.5169867,0.0001549857,0.0001984083,0.003113192,0.004474725,0.004023192,0.002656994,0.04549142,0.001976417,0.4164635],"study_design_scores_gemma":[0.01766432,0.002426175,0.7651139,0.001285965,0.0001650731,0.001393279,0.00242323,0.1428063,0.0001337416,0.002378339,0.06359677,0.0006128213],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854383,0.0003688328,0.001749681,0.009676711,0.0007199275,0.0002164251,0.000001370453,0.00005707854,0.001771652],"genre_scores_gemma":[0.993764,0.00003522169,0.0007679192,0.004294957,0.001013371,0.000007842403,0.00006099882,0.000008214913,0.00004747724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4158507,"threshold_uncertainty_score":0.4359398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1168611268011388,"score_gpt":0.4083649910787789,"score_spread":0.29150386427764,"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."}}