{"id":"W2161368485","doi":"10.1109/tbme.2011.2134096","title":"An MRI-Compatible Robotic System With Hybrid Tracking for MRI-Guided Prostate Intervention","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Princess Margaret Cancer Centre; University of Toronto","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Fiducial marker; Imaging phantom; Scanner; Interfacing; Magnetic resonance imaging; Computer science; Tracking (education); Computer vision; Tracking system; Artificial intelligence; Prostate; Interventional magnetic resonance imaging; Biomedical engineering; Medicine; Computer hardware; Kalman filter; Nuclear medicine; Radiology","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":[0.00013428,0.0002367489,0.0002341524,0.0002492341,0.0001057183,0.00004462591,0.000177759,0.00007867565,0.0000229166],"category_scores_gemma":[0.000001909143,0.0002235523,0.0001071145,0.0003040656,0.00003581512,0.000183726,7.002798e-7,0.0002196165,0.00001613859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001239238,"about_ca_system_score_gemma":0.00001645929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002938905,"about_ca_topic_score_gemma":0.000007215809,"domain_scores_codex":[0.9988208,0.000008477073,0.0003641428,0.0002670739,0.0001879356,0.0003515802],"domain_scores_gemma":[0.9993485,0.00004644219,0.00003457625,0.0002871933,0.00005489956,0.0002284371],"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":[0.00001721664,0.000189956,0.00000363008,0.0003589485,0.00009905146,0.000006106447,0.0002247385,0.9887247,0.00506877,0.0002740809,0.00005536367,0.00497745],"study_design_scores_gemma":[0.000708255,0.0002631451,0.0000858858,0.0002811123,0.00007204399,0.0000458914,0.00009116242,0.9620018,0.0358518,0.00001011292,0.0002960368,0.0002927693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01766767,0.0000253667,0.9799562,0.00002127955,0.0006041919,0.0005322422,0.00004061653,0.001097378,0.0000550501],"genre_scores_gemma":[0.9663391,0.00001317555,0.0330329,0.000006512317,0.00006871829,0.0004066579,0.00002592508,0.00008916208,0.00001787554],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9486714,"threshold_uncertainty_score":0.9116198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02028532068096087,"score_gpt":0.2300905414421485,"score_spread":0.2098052207611877,"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."}}