{"id":"W2149832588","doi":"10.1007/978-3-642-15711-0_48","title":"MRI-Guided Robotic Prostate Biopsy: A Clinical Accuracy Validation","year":2010,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute; National Institutes of Health","keywords":"Prostate cancer; Magnetic resonance imaging; Prostate; Displacement (psychology); Medicine; Computer science; Biopsy; Computer vision; Prostate biopsy; Artificial intelligence; Radiology; Cancer","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":[0.0004512114,0.0001121403,0.000124641,0.000116489,0.00009347451,0.0001619387,0.0004361882,0.0000733849,0.000009034759],"category_scores_gemma":[0.0002350685,0.0001007706,0.00003395099,0.0008492545,0.0001883096,0.0001729237,0.00008655552,0.000361261,0.00003495738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002411754,"about_ca_system_score_gemma":0.00006042847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000801167,"about_ca_topic_score_gemma":0.00001110239,"domain_scores_codex":[0.9989552,0.00001170472,0.000277355,0.0003146933,0.0001755268,0.0002654893],"domain_scores_gemma":[0.9990562,0.000340971,0.00003744576,0.0004104166,0.0000681095,0.00008685269],"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":[5.323753e-7,0.00002524721,0.007842828,0.000007559583,0.000002169904,0.000002105657,0.0001625539,0.8942822,0.01207631,0.0001589984,0.00003359919,0.08540584],"study_design_scores_gemma":[0.000131464,0.00001266032,0.01399248,0.00001202791,0.000002836967,0.00001835819,1.711709e-7,0.9701774,0.01065227,0.004743375,0.000115317,0.0001416395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2708623,0.000009063299,0.7270844,0.0004272207,0.00132235,0.0001475492,5.345757e-7,0.000131466,0.00001508679],"genre_scores_gemma":[0.7442513,0.000005922265,0.2554242,0.0001092481,0.0001871325,0.0000104436,0.000002372843,0.000008868594,4.919973e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.473389,"threshold_uncertainty_score":0.4109305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02650892056530584,"score_gpt":0.3154139528475287,"score_spread":0.2889050322822228,"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."}}