{"id":"W2995336252","doi":"10.1093/ons/opz359","title":"Artificial Neural Networks to Assess Virtual Reality Anterior Cervical Discectomy Performance","year":2019,"lang":"en","type":"article","venue":"Operative Neurosurgery","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal General Hospital; McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Artificial neural network; Anterior cervical discectomy and fusion; Virtual reality; Metric (unit); Computer science; Artificial intelligence; Discectomy; Machine learning; Medicine; Cervical spine; Surgery; Operations management; Lumbar","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003062333,0.0002116508,0.000466471,0.00008602086,0.0001471819,0.00009304343,0.00007763803,0.00008714559,0.000949873],"category_scores_gemma":[0.0001161397,0.0001663143,0.000139693,0.000373631,0.00005590474,0.000196262,0.00007539961,0.0003638737,0.0001776156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004402173,"about_ca_system_score_gemma":0.00004558466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006533141,"about_ca_topic_score_gemma":0.000004124312,"domain_scores_codex":[0.9982433,0.0001702148,0.000432017,0.000457776,0.0003174067,0.0003792602],"domain_scores_gemma":[0.9989045,0.0003543183,0.00005659899,0.0002769272,0.0001072729,0.0003003787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003236801,0.0004556739,0.8448451,0.00005050366,0.00006999129,0.0002591213,0.0005262458,0.05753544,0.01074309,0.0004127303,0.0003539661,0.0815114],"study_design_scores_gemma":[0.0008644362,0.0005567785,0.6538325,0.00008977721,0.0000225345,0.00003681991,0.00007785192,0.3394373,0.00149998,0.000001836919,0.003309631,0.0002706122],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933581,0.000007288655,0.0005766227,0.00167216,0.001076885,0.0004426231,0.000007039744,0.00009413531,0.002765151],"genre_scores_gemma":[0.9948174,0.000006951174,0.00001920953,0.003978284,0.0003892912,0.00002167448,0.00002763225,0.00003019219,0.0007093264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2819018,"threshold_uncertainty_score":0.9999634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06649530957308088,"score_gpt":0.3388617671454934,"score_spread":0.2723664575724126,"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."}}