{"id":"W4415402809","doi":"10.2196/73162","title":"Survival Prediction for Postoperative Patients With Kidney Cancer Based on Computed Tomography Radiomics: Retrospective Cohort Study","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Retrospective cohort study; Nomogram; Kidney cancer; Computed tomography; Radiomics; Cancer; Kidney disease; Survival analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001389866,0.0003308497,0.0004749068,0.001113042,0.0004617427,0.0006405553,0.0004424314,0.0003378348,0.0009593213],"category_scores_gemma":[0.002128469,0.0003821822,0.0008906721,0.001361421,0.0003223695,0.0005081146,0.000602661,0.0006123681,0.0002372904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005156067,"about_ca_system_score_gemma":0.0007727138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006438691,"about_ca_topic_score_gemma":0.008750842,"domain_scores_codex":[0.9993767,0.0001014601,0.00008871751,0.0002175478,0.0001186882,0.0000969223],"domain_scores_gemma":[0.9983898,0.000257431,0.0005012634,0.0004112025,0.0002543486,0.0001858781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000826771,0.00001385317,0.9990031,0.000004409123,0.00004241776,0.00005291844,0.00002725481,0.00005230656,0.00007815902,0.00001102379,0.00006302851,0.0005687933],"study_design_scores_gemma":[0.00001194289,0.000137868,0.9977869,0.000007994334,0.0001259407,0.000424346,0.0001480995,0.0007671,0.0001221542,0.0000340519,0.0004231871,0.00001045574],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998588,0.0001288554,0.0003848183,0.00001563316,0.000005035685,0.00001861029,0.0007118494,0.000004699584,0.0001424276],"genre_scores_gemma":[0.9981576,0.0001037156,0.000234887,0.0000151095,0.000006996332,0.00002834533,0.001355921,0.000003719883,0.00009375783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006438691,"threshold_uncertainty_score":0.01280242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005151724629763385,"score_gpt":0.2864015859368195,"score_spread":0.2812498613070561,"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."}}