{"id":"W3157229268","doi":"10.1097/ju.0000000000001832","title":"Optimizing Spatial Biopsy Sampling for the Detection of Prostate Cancer","year":2021,"lang":"en","type":"article","venue":"The Journal of Urology","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of General Medical Sciences; National Cancer Institute","keywords":"Medicine; Biopsy; Prostate cancer; Prostatectomy; Prostate biopsy; Sampling (signal processing); Radiology; Magnetic resonance imaging; Prostate; Cancer detection; Cancer; Urology; Internal medicine","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":[0.004385157,0.0004538086,0.0005419178,0.0009437739,0.0002739284,0.0005472051,0.0006045054,0.0003711672,0.0005377487],"category_scores_gemma":[0.01523927,0.0003033827,0.0003599866,0.000688182,0.0004013159,0.0004634915,0.0005891897,0.0001969184,0.0002630537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004262475,"about_ca_system_score_gemma":0.0009034523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001992614,"about_ca_topic_score_gemma":0.00574736,"domain_scores_codex":[0.996048,0.002156847,0.0003477998,0.0005284197,0.0008103359,0.000108467],"domain_scores_gemma":[0.9934255,0.003142977,0.001799087,0.0006131904,0.0008512421,0.0001679416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001193962,0.0001372484,0.8138418,0.0003461174,0.0002345707,0.0001419823,0.0001890242,0.004621008,0.04641489,0.0001467116,0.0004647895,0.1322678],"study_design_scores_gemma":[0.0001240969,0.002285401,0.8975764,0.0001298903,0.0004815367,0.003283244,0.0003672766,0.04077654,0.05174507,0.0004636563,0.002708227,0.00005867885],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.933251,0.004693846,0.06011806,0.0001790133,0.00002745289,0.0002055008,0.000235324,0.0002505572,0.001039237],"genre_scores_gemma":[0.9634008,0.0005073345,0.0355936,0.00007572961,0.00002229842,0.00007734558,0.0001880087,0.00002154448,0.0001133079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004385157,"threshold_uncertainty_score":0.02319121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03395341424571971,"score_gpt":0.3149055453714773,"score_spread":0.2809521311257576,"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."}}