{"id":"W2151323610","doi":"10.1007/s11548-013-0831-9","title":"Accuracy analysis in MRI-guided robotic prostate biopsy","year":2013,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre; Centre Hospitalier de l’Université de Montréal; Queen's University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute","keywords":"Prostate biopsy; Prostate; Computer science; Biopsy; Multiparametric MRI; Radiology; Health informatics; Medicine; Artificial intelligence; Medical physics; Computer vision; Pathology; Internal medicine; Public health; 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":[0.0003021031,0.0001288973,0.0004619274,0.0007310927,0.00003006169,0.00005731758,0.0002055241,0.00005105538,0.000200109],"category_scores_gemma":[0.00001408068,0.0001051948,0.0002318723,0.000241755,0.00008744861,0.0003533262,0.00003224119,0.000215645,0.000001492959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004057409,"about_ca_system_score_gemma":0.00005483149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005263013,"about_ca_topic_score_gemma":7.462205e-7,"domain_scores_codex":[0.9987822,0.0001439196,0.0006316,0.0001454061,0.0001366258,0.0001603171],"domain_scores_gemma":[0.9984554,0.0006426048,0.0004727825,0.00009588214,0.0002680645,0.00006528055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003771002,0.0001059861,0.9186645,0.000002436147,0.001835412,0.00007668143,0.0001382109,0.004234599,0.0005725084,0.0004168064,0.002522046,0.07139306],"study_design_scores_gemma":[0.0007205075,0.00005140131,0.9687262,0.00007342696,0.0001022575,0.0007013938,0.00003209061,0.02374672,0.0002581953,0.004011937,0.001308723,0.0002672114],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4916058,0.000293225,0.5063844,0.001089216,0.000481916,0.00006999707,0.000002655125,0.00001191714,0.00006086618],"genre_scores_gemma":[0.9692982,0.0001265491,0.02989173,0.0002258809,0.0003994328,0.000007478704,0.00001588581,0.000008714502,0.00002613452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4776924,"threshold_uncertainty_score":0.4289718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01376261013178529,"score_gpt":0.28905828114228,"score_spread":0.2752956710104947,"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."}}