{"id":"W3110385255","doi":"10.1016/j.surg.2020.10.039","title":"Computer vision in surgery","year":2020,"lang":"en","type":"article","venue":"Surgery","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":160,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"SAFER; Interpretability; Artificial intelligence; Medicine; Deep learning; Identification (biology); Variety (cybernetics); Machine learning; Deep neural networks; Artificial neural network; Computer science; Data science; Computer security","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":[0.0002674523,0.00006719818,0.0002893146,0.00009544651,0.00001518813,0.000009540093,0.00001442054,0.00004698258,0.0006296316],"category_scores_gemma":[0.0002184852,0.00005757085,0.0001341337,0.0003172451,0.00001747787,0.00004991821,0.00001567284,0.0001078844,0.0001589853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001236713,"about_ca_system_score_gemma":0.00004708931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000580602,"about_ca_topic_score_gemma":8.135601e-7,"domain_scores_codex":[0.9992613,0.00003972271,0.0002435438,0.000155566,0.0001496963,0.0001501688],"domain_scores_gemma":[0.9985098,0.001214615,0.00003023632,0.00007139414,0.00002067219,0.0001532938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003067529,0.0000815649,0.7494069,0.00006042065,0.0000144143,0.0007359798,0.0001586617,0.0001130516,0.00005248027,0.00005088499,0.01118309,0.2378358],"study_design_scores_gemma":[0.0008451525,0.00003455004,0.8564447,0.0001985525,0.000008695652,0.00001572937,0.00003171801,0.01507295,0.0001329219,0.00003301279,0.1270165,0.000165521],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99164,0.00008077421,0.0002361327,0.004719945,0.0002565611,0.00006385092,7.273525e-7,0.00009568399,0.002906357],"genre_scores_gemma":[0.9936246,0.00001402479,0.00009163301,0.005920615,0.0002886247,0.000001625066,0.0000228629,0.00001038962,0.00002565357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2376703,"threshold_uncertainty_score":0.6894024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07249876176651723,"score_gpt":0.3066257798265966,"score_spread":0.2341270180600794,"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."}}