{"id":"W2007732707","doi":"10.1016/j.juro.2015.02.2557","title":"PD44-11 UNDERSTANDING THE PERFORMANCE OF ACTIVE SURVEILLANCE SELECTION CRITERIA IN REAL-WORLD PRACTICE","year":2015,"lang":"en","type":"article","venue":"The Journal of Urology","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Guideline; Miller; Clinical Practice; Selection (genetic algorithm); Cohort; Demographics; Family medicine; Demography; Pathology; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04914133,0.0002525183,0.0006354159,0.003348838,0.001071869,0.004338024,0.001645919,0.001341749,0.004745704],"category_scores_gemma":[0.2829251,0.0004487868,0.0006847447,0.004672983,0.001098232,0.00298579,0.002874504,0.001547128,0.001250554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001971701,"about_ca_system_score_gemma":0.003661003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009882426,"about_ca_topic_score_gemma":0.01191813,"domain_scores_codex":[0.951242,0.02609262,0.007479182,0.002911748,0.01055883,0.00171549],"domain_scores_gemma":[0.6969565,0.1830299,0.05824747,0.01063538,0.04293155,0.00819932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001103727,0.00009393302,0.9204113,0.000162555,0.00009619617,0.00002947346,0.0006426572,0.0005061277,0.00002809473,0.0006474879,0.01860759,0.05866425],"study_design_scores_gemma":[0.0001022775,0.0005707054,0.9539647,0.001149728,0.0001342373,0.0003345473,0.002740318,0.009419755,0.0002812073,0.002531503,0.02868282,0.0000882224],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8509821,0.01080012,0.01398156,0.05992058,0.001137949,0.00111077,0.01148457,0.0003384596,0.05024401],"genre_scores_gemma":[0.981475,0.002124734,0.007819255,0.002760664,0.0006647346,0.0006571566,0.003266777,0.00007830299,0.001153424],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04914133,"threshold_uncertainty_score":0.2598873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4263754414938367,"score_gpt":0.4530756507796798,"score_spread":0.0267002092858431,"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."}}