{"id":"W1595297024","doi":"10.1109/icra.2015.7139945","title":"Tissue compliance determination using a da Vinci instrument","year":2015,"lang":"en","type":"article","venue":"","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Biological tissue; Biomedical engineering; Stiffness; Surgical robot; Tendon; Computer science; Medical robotics; Da Vinci Surgical System; Compliance (psychology); Surgical procedures; Robot; Materials science; Artificial intelligence; Engineering; Surgery; Robotic surgery; Medicine; Composite material","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":[0.00002664043,0.00004322728,0.00004120651,0.00001775199,0.00001946672,0.00002297696,0.0000487442,0.00001808901,0.00001832326],"category_scores_gemma":[0.000003633772,0.00004408161,0.000006340016,0.00006778386,0.000007057428,0.0000479739,0.00001238453,0.00002516923,0.00007014129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006531994,"about_ca_system_score_gemma":0.000007191099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002235386,"about_ca_topic_score_gemma":0.000007695588,"domain_scores_codex":[0.9997475,0.000001823176,0.00006893065,0.00005243996,0.00005801088,0.0000712765],"domain_scores_gemma":[0.9998195,0.000005812907,0.00000752266,0.00009580893,0.00002348096,0.00004793238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000281162,0.0000803949,0.001345915,0.00007951671,0.00002427986,0.000005109974,0.0006578394,0.5977599,0.05830808,0.02116022,0.003842843,0.3167331],"study_design_scores_gemma":[0.0001147259,0.000009210771,0.0003076776,0.000008980115,0.000004071866,0.000005744467,0.00003731205,0.9759348,0.01156094,0.000642886,0.01128402,0.0000896172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2654864,0.00004425425,0.7195862,0.00007836505,0.0001660654,0.0001294062,0.000002132648,0.0002388556,0.01426836],"genre_scores_gemma":[0.9510884,0.000001543014,0.04872566,0.00002141021,0.0000337464,0.000007577869,0.000003273718,0.000008575547,0.0001098682],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6856019,"threshold_uncertainty_score":0.1797596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1434739268903501,"score_gpt":0.320566473101045,"score_spread":0.1770925462106949,"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."}}