{"id":"W4404860873","doi":"10.5489/cuaj.9052","title":"Upping the ante (with machine-learning) for patients with UTUC","year":2024,"lang":"en","type":"article","venue":"Canadian Urological Association Journal","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0002151982,0.0004213885,0.0006657693,0.0005933886,0.0006969649,0.0007582338,0.0003916527,0.0007997207,0.007458797],"category_scores_gemma":[0.001464466,0.0001281205,0.001180595,0.0004064882,0.0003323716,0.0009668064,0.0006119384,0.001766215,0.0009090079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000381267,"about_ca_system_score_gemma":0.0008514012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002638588,"about_ca_topic_score_gemma":0.005946622,"domain_scores_codex":[0.9998443,0.00002682685,0.00001355787,0.00002659966,0.00003446548,0.00005419426],"domain_scores_gemma":[0.9997271,0.00006665487,0.00004971884,0.00002045378,0.0000293163,0.0001068392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002363334,0.0009484258,0.2713441,0.0008776085,0.0001740501,0.00686408,0.0001286232,0.002808149,0.002867619,0.0007422593,0.01912987,0.6917521],"study_design_scores_gemma":[0.0007412288,0.01271213,0.7611175,0.005368491,0.001687429,0.06382873,0.002276888,0.02987015,0.01434047,0.01718037,0.09054866,0.0003279215],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8065452,0.08974942,0.02644604,0.02210059,0.003815576,0.0006148134,0.002614456,0.001077508,0.04703634],"genre_scores_gemma":[0.9614009,0.01682581,0.01123641,0.002689836,0.001015838,0.0001378005,0.002145998,0.00008811982,0.004459325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007458797,"threshold_uncertainty_score":0.02495217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007004269583434344,"score_gpt":0.2243939183956674,"score_spread":0.217389648812233,"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."}}