{"id":"W4393807014","doi":"10.5281/zenodo.10526884","title":"Dataset related to article \"Diagnostic accuracy of multiparametric MRI- and microultrasound-targeted biopsy in biopsy-naïve patients with a PI-RADS 5 lesion: a single-institutional study \"","year":2024,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University and Génome Québec Innovation Centre","funders":"","keywords":"Biopsy; Medicine; Radiology; Diagnostic accuracy; Multiparametric MRI; Lesion; Nuclear medicine; Pathology; Internal medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001663834,0.0008351384,0.001428453,0.001787908,0.0007401634,0.001260919,0.00217043,0.001520751,0.07079349],"category_scores_gemma":[0.01982691,0.00043723,0.00126401,0.003602117,0.0003549686,0.0007592862,0.001308318,0.001177506,0.01669188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001354955,"about_ca_system_score_gemma":0.003629454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01178375,"about_ca_topic_score_gemma":0.02033907,"domain_scores_codex":[0.9979213,0.0003157742,0.0006871355,0.0004818795,0.0004141668,0.0001797113],"domain_scores_gemma":[0.9813873,0.006507543,0.004306779,0.002447677,0.004498365,0.0008522699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002174006,0.0002720995,0.03784572,0.003394185,0.0005152881,0.0002199806,0.00005899208,0.0007615661,0.000474412,0.000522949,0.9388975,0.01486327],"study_design_scores_gemma":[0.01114769,0.001047514,0.3491051,0.004173194,0.001238972,0.001830478,0.000503214,0.003859076,0.002857464,0.002665973,0.6212404,0.0003308441],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001850056,0.00005610981,0.0001330002,0.0001451339,0.00004095438,0.0001641503,0.9966169,0.00008926094,0.0009045411],"genre_scores_gemma":[0.007986175,0.00008143114,0.0005965338,0.0002609399,0.00003821998,0.001130112,0.9888583,0.00003303719,0.001015177],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07079349,"threshold_uncertainty_score":0.2368278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02110995481476756,"score_gpt":0.2889513982208436,"score_spread":0.2678414434060761,"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."}}