{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004538951,0.0003369336,0.0007085745,0.0001026589,0.0001502195,0.0001980642,0.0004296371,0.0001761194,0.0001944873],"category_scores_gemma":[0.001131591,0.0002542734,0.0001228773,0.0004550518,0.0002944502,0.0008146658,0.0004546868,0.0003611218,0.0003012052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003181085,"about_ca_system_score_gemma":0.0007944705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002920566,"about_ca_topic_score_gemma":0.0007137773,"domain_scores_codex":[0.9967225,0.00006405658,0.001683334,0.0004321729,0.0005690305,0.000528861],"domain_scores_gemma":[0.9961644,0.0003717474,0.0005916986,0.001291427,0.0008800745,0.0007006667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001767449,0.0009458258,0.6860775,0.0003002779,0.0005617759,0.00003469377,0.007106326,0.000003250477,0.000003263118,0.00006304948,0.1467213,0.1564153],"study_design_scores_gemma":[0.006479697,0.006128573,0.7119821,0.001532528,0.000533208,0.00001074439,0.0009663944,0.0006167819,0.0008975294,0.0001362726,0.2701301,0.0005861394],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838304,0.0001173223,0.00004859324,0.006380592,0.001372081,0.002371685,0.004691343,0.00008584902,0.001102146],"genre_scores_gemma":[0.9730574,0.004058666,0.006065706,0.01288183,0.0008212571,0.0004438254,0.001372653,0.00006191405,0.001236666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1558292,"threshold_uncertainty_score":0.9999909,"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."}}