{"id":"W1983113631","doi":"10.1111/j.1464-410x.2008.08073.x","title":"Can nomograms be superior to other prediction tools?","year":2008,"lang":"en","type":"article","venue":"British Journal of Urology","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":149,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Nomogram; Computer science; Medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001005848,0.00007401565,0.0002674815,0.00007080074,0.00006102088,0.00001474885,0.00005301671,0.00006383432,0.0002386083],"category_scores_gemma":[0.00007630346,0.0000737873,0.000101897,0.00008307432,0.00006050453,0.00006557339,0.00001386837,0.0001649408,0.000007554268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008570237,"about_ca_system_score_gemma":0.0001261722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001866615,"about_ca_topic_score_gemma":0.0001327068,"domain_scores_codex":[0.999222,0.00003208054,0.000287728,0.0001144458,0.0001538534,0.0001898485],"domain_scores_gemma":[0.9994477,0.0000291773,0.00008196862,0.00008051711,0.0001479666,0.0002126754],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001137676,0.001419734,0.558224,0.00003150117,0.0006266897,0.02324268,0.001249129,0.0001331147,0.00176175,0.00004923368,0.07508077,0.3370437],"study_design_scores_gemma":[0.003481965,0.00556759,0.6963193,0.00008832976,0.0001621953,0.08076509,0.00005155827,0.000007914648,0.0006827341,0.00003846002,0.2127455,0.00008934394],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862049,0.002735087,0.00008029601,0.009835947,0.0003641977,0.0001786835,0.0001102548,0.00001292299,0.0004777265],"genre_scores_gemma":[0.9910892,0.001950091,0.0003755526,0.005977067,0.0003988332,0.00001492125,0.000009647142,0.00001680769,0.0001679391],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3369544,"threshold_uncertainty_score":0.3008959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02613730296346314,"score_gpt":0.2563009380088959,"score_spread":0.2301636350454327,"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."}}