{"id":"W4304891567","doi":"10.3389/fruro.2022.1024662","title":"The state of artificial intelligence in pediatric urology","year":2022,"lang":"en","type":"article","venue":"Frontiers in Urology","topic":"Pediatric Urology and Nephrology Studies","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Izaak Walton Killam Health Centre; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Pediatric urology; Vesicoureteral reflux; Usability; Pyeloplasty; Computer science; Interpretability; Context (archaeology); MEDLINE; Urology; Medicine; Medical physics; Artificial intelligence; Hydronephrosis; General surgery; Internal medicine; Urinary system","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":[0.001062393,0.000137208,0.0004411621,0.0005559503,0.0001651452,0.000001288089,0.0002767841,0.0001202857,0.00004273412],"category_scores_gemma":[0.0003135998,0.0001170308,0.00006531112,0.0007313383,0.0005665062,0.00002122408,0.0002621469,0.000930022,0.000005975712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005424211,"about_ca_system_score_gemma":0.0001635349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002630514,"about_ca_topic_score_gemma":0.0001768884,"domain_scores_codex":[0.9979262,0.000497447,0.0005841165,0.0003286707,0.0001424666,0.0005210947],"domain_scores_gemma":[0.9990293,0.0004638307,0.000169638,0.000266583,0.00003108168,0.00003958318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007950178,0.0006101071,0.907522,0.00001129573,0.0002587027,0.0006467684,0.001866558,0.003341798,0.00004532527,0.001714078,0.01172889,0.06430431],"study_design_scores_gemma":[0.002461821,0.007240599,0.7807652,5.413609e-7,0.0005677609,0.0004395867,0.001305205,0.01511569,0.00007439912,0.1529315,0.03861918,0.0004784841],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9602671,0.02664284,0.00202924,0.007789091,0.00233642,0.0003796341,0.00001020351,0.00002512689,0.0005203839],"genre_scores_gemma":[0.9930251,0.004517168,0.0003563266,0.001766468,0.0001183416,0.0001290689,0.000005447595,0.000012427,0.00006968626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1512174,"threshold_uncertainty_score":0.4772379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01238691772371918,"score_gpt":0.2527081210441078,"score_spread":0.2403212033203886,"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."}}