{"id":"W4390618218","doi":"10.1007/s00464-023-10551-7","title":"Defining digital surgery: a SAGES white paper","year":2024,"lang":"en","type":"article","venue":"Surgical Endoscopy","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University; University of Toronto; University Health Network","funders":"","keywords":"Terminology; Scope (computer science); Computer science; Robotic surgery; Instrumentation (computer programming); Space (punctuation); Data science; Medicine; 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002381955,0.0001819871,0.0003739221,0.0001424579,0.00006280945,0.0001910492,0.00004358337,0.0001077722,0.005633532],"category_scores_gemma":[0.0001877824,0.0001355578,0.0003152269,0.0004556585,0.00007890186,0.0002513458,0.00003421367,0.0002943721,0.001119322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003597491,"about_ca_system_score_gemma":0.0001017305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004685881,"about_ca_topic_score_gemma":6.219937e-7,"domain_scores_codex":[0.9985608,0.00003166264,0.0003213816,0.0003605306,0.0003583836,0.0003672594],"domain_scores_gemma":[0.9981295,0.001357315,0.00002310777,0.0001702273,0.00003366352,0.0002861945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003012268,0.0005861725,0.2392026,0.0007090034,0.000824218,0.01949918,0.001294607,0.00015639,0.0001009047,0.08582172,0.01111561,0.6376773],"study_design_scores_gemma":[0.004652881,0.00007752415,0.003963356,0.0004822887,0.00005677704,0.0002871999,0.0001042818,0.001243687,0.0001206985,0.0005580052,0.9882113,0.0002420065],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.571722,0.005096952,0.0001054178,0.002515229,0.000849152,0.0002005013,0.00003695811,0.001086521,0.4183873],"genre_scores_gemma":[0.9958668,0.00007584174,0.00009632258,0.0004075961,0.0002669448,0.00001313378,0.0001035106,0.00003906292,0.003130776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9770957,"threshold_uncertainty_score":0.9996584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01908554815818449,"score_gpt":0.300821000482399,"score_spread":0.2817354523242145,"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."}}