{"id":"W4414524711","doi":"10.1002/bco2.70080","title":"AI‐driven preoperative risk assessment in kidney cancer surgery: A comparative feasibility study of machine learning models","year":2025,"lang":"en","type":"article","venue":"BJUI Compass","topic":"Renal cell carcinoma treatment","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Kidney cancer; Risk assessment; Risk stratification; Kidney disease; Cancer; Renal cell carcinoma","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02749832,0.001038193,0.0008655966,0.001745411,0.0003118826,0.001965449,0.001241027,0.001078552,0.001669191],"category_scores_gemma":[0.06680843,0.0003854916,0.001699102,0.00110105,0.0005075113,0.002318301,0.001267739,0.001452979,0.0003749429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001375871,"about_ca_system_score_gemma":0.001606424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0039071,"about_ca_topic_score_gemma":0.001790022,"domain_scores_codex":[0.9900076,0.00806177,0.0003729857,0.0006726249,0.0006904024,0.0001945743],"domain_scores_gemma":[0.9151465,0.07705039,0.002074428,0.001862885,0.00303478,0.0008310254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01571433,0.003907137,0.3630309,0.0009959536,0.002496905,0.0002306482,0.0006979556,0.3621289,0.0007147478,0.005049173,0.002455279,0.2425781],"study_design_scores_gemma":[0.0002133403,0.003320118,0.02862286,0.0001242982,0.0003039583,0.0001114046,0.0002468775,0.9628739,0.0004326214,0.00299826,0.000703272,0.00004908478],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9458744,0.00220607,0.04671423,0.0009869559,0.000156808,0.0004852691,0.0006437922,0.0002746663,0.002657757],"genre_scores_gemma":[0.9861292,0.0004178288,0.01249943,0.00006900736,0.00005618145,0.0001416704,0.0004154341,0.00002081031,0.0002504123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02749832,"threshold_uncertainty_score":0.1454268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09627797162637906,"score_gpt":0.3801719645597508,"score_spread":0.2838939929333718,"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."}}