{"id":"W2909104665","doi":"10.1109/iros.2018.8593523","title":"Development and validation of MRI compatible pediatric surgical robot with modular tooling for bone biopsy","year":2018,"lang":"en","type":"article","venue":"","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; SickKids Foundation; Hospital for Sick Children","funders":"","keywords":"Biopsy; Magnetic resonance imaging; Breast biopsy; Modular design; Radiology; Interventional magnetic resonance imaging; Computer science; Medicine; Biomedical engineering; Cancer; Breast cancer; Mammography","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":[0.00005351536,0.00005236326,0.00007872107,0.0000395204,0.00004019443,0.00001099571,0.00002543031,0.00002159694,0.00000870024],"category_scores_gemma":[0.00000185556,0.0000437199,0.000008208885,0.0001059035,0.00001297472,0.00002895118,0.000008256484,0.00001658401,0.000002569939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008129492,"about_ca_system_score_gemma":0.00000903137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003298665,"about_ca_topic_score_gemma":0.000002685682,"domain_scores_codex":[0.9996721,0.000001288016,0.0001209076,0.00007430743,0.00005233628,0.0000790381],"domain_scores_gemma":[0.9998148,0.00002632363,0.00001716345,0.00006521028,0.00004839518,0.0000280789],"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":[0.00007701001,0.0003190571,0.05317205,0.0009451033,0.000220137,0.000003897002,0.002023638,0.8492213,0.0390248,0.04450095,0.001015217,0.009476864],"study_design_scores_gemma":[0.001724345,0.0001291457,0.01598902,0.00003016641,0.00008458061,0.00002776499,0.0001321081,0.6497201,0.3239882,0.0003588727,0.007301973,0.0005136231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6303239,0.00002906557,0.3692625,0.000011429,0.00001405926,0.00009823616,0.000001196745,0.00004016253,0.0002194012],"genre_scores_gemma":[0.7676302,0.000008090166,0.2322515,0.000001496741,0.00005082647,0.00001805504,0.00001041011,0.000007746591,0.00002171852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2849634,"threshold_uncertainty_score":0.1782846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01779044609768749,"score_gpt":0.2316206688504789,"score_spread":0.2138302227527915,"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."}}