{"id":"W2889076010","doi":"10.1200/cci.17.00143","title":"Can We Use Administrative Data to Accurately Identify Patients Who Receive a Prostate Biopsy?","year":2018,"lang":"en","type":"article","venue":"JCO Clinical Cancer Informatics","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Medicine; Prostate cancer; False positive paradox; Biopsy; Prostate; Cancer registry; Diagnosis code; Prostate biopsy; Population; Cancer; Radiology; Internal medicine; Artificial intelligence; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01857813,0.0006800905,0.001187566,0.00330873,0.0007907893,0.002755401,0.002507405,0.001470169,0.001773269],"category_scores_gemma":[0.1786224,0.0006933801,0.001474015,0.005095898,0.001321633,0.00300004,0.001436476,0.001314485,0.0009613415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007720951,"about_ca_system_score_gemma":0.01472591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4987051,"about_ca_topic_score_gemma":0.5038664,"domain_scores_codex":[0.9867884,0.007490627,0.001041758,0.001036218,0.002806819,0.0008361982],"domain_scores_gemma":[0.9162289,0.03635084,0.02224363,0.009607536,0.01301257,0.002556495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001777249,0.00006703166,0.9421813,0.0003009774,0.000483309,0.00006095897,0.0003228865,0.004874777,0.00004019654,0.001095422,0.01209832,0.03829702],"study_design_scores_gemma":[0.0003248005,0.0002959981,0.8609093,0.002000045,0.0009573332,0.0003132868,0.001631686,0.082361,0.0004988243,0.012346,0.03814354,0.0002181675],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6512029,0.03126617,0.04048124,0.1918658,0.001904589,0.0008816072,0.0546261,0.00104123,0.0267305],"genre_scores_gemma":[0.9721267,0.004227587,0.009534394,0.004790488,0.0005073145,0.0001392151,0.00805277,0.00004869492,0.0005727045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4987051,"threshold_uncertainty_score":0.9916047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3799831266947138,"score_gpt":0.5101511465507244,"score_spread":0.1301680198560106,"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."}}