{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001028401,0.0006598824,0.0005061667,0.001512463,0.0004961504,0.0005742804,0.0008933751,0.0006795326,0.002108343],"category_scores_gemma":[0.002318479,0.0005606423,0.0002767517,0.0007214466,0.0003861322,0.0007142523,0.0008140058,0.0005880853,0.0008568766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002961058,"about_ca_system_score_gemma":0.000738221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006646715,"about_ca_topic_score_gemma":0.001551244,"domain_scores_codex":[0.998551,0.0001640398,0.0001217384,0.0003908956,0.0006991284,0.00007327364],"domain_scores_gemma":[0.9985614,0.0005061095,0.0002130491,0.0002859964,0.0003660927,0.00006739138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001280378,0.00003552276,0.003457281,0.0002812614,0.00002986636,0.0001326339,0.0001772852,0.001439941,0.8463174,0.001389907,0.0007192265,0.1458917],"study_design_scores_gemma":[0.00006199614,0.0007908,0.04928365,0.00008606155,0.0001098301,0.004570411,0.0002062573,0.1022928,0.8102443,0.001174867,0.03083917,0.00033987],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05740011,0.0005803,0.9382089,0.00008360819,0.00007841187,0.0001516544,0.0002397521,0.001430276,0.001827068],"genre_scores_gemma":[0.1938846,0.0004508374,0.802208,0.00009686103,0.000026986,0.0002376662,0.0002783567,0.0001944283,0.002622123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002108343,"threshold_uncertainty_score":0.007053077,"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."}}