{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000495915,0.0002934665,0.001191867,0.0003026697,0.0001037953,0.00002158246,0.0001162223,0.0000627156,0.00008780387],"category_scores_gemma":[0.00008222822,0.0002363964,0.0001466268,0.0005711509,0.00007691411,0.0001212691,0.0001294573,0.00065191,0.000002964649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000841725,"about_ca_system_score_gemma":0.0008483459,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008912439,"about_ca_topic_score_gemma":0.003467186,"domain_scores_codex":[0.9971977,0.0009024318,0.0006601518,0.0005403838,0.0004159007,0.0002834346],"domain_scores_gemma":[0.9984506,0.0003861666,0.000239038,0.000441447,0.0003216262,0.0001611552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007364527,0.003819039,0.9602911,0.0001366831,0.0004758299,0.00004033854,0.00383336,0.02925922,0.0002420471,0.0001028297,0.0009121301,0.0001509318],"study_design_scores_gemma":[0.004214457,0.0007044091,0.8237237,0.000387758,0.0003003124,0.000001972473,0.001729884,0.1676476,0.0009402955,0.0001357798,0.00006331683,0.0001504121],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926913,0.0004316202,0.0005959037,0.0007586392,0.0001112798,0.002394952,0.00006244763,0.00004450342,0.00290929],"genre_scores_gemma":[0.9986035,0.00004706642,0.0003004959,0.0001623736,0.00001874546,0.0003807998,0.00005604825,0.0000151975,0.0004157533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1383884,"threshold_uncertainty_score":0.9976873,"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."}}