{"id":"W3127445005","doi":"10.2196/25635","title":"Machine Learning Approach to Predict the Probability of Recurrence of Renal Cell Carcinoma After Surgery: Prediction Model Development Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Renal cell carcinoma treatment","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Renal cell carcinoma; Nephrectomy; Medicine; Receiver operating characteristic; Naive Bayes classifier; Cohort; Surgery; Machine learning; Internal medicine; Database; Computer science; Kidney; Support vector machine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001350419,0.0002258538,0.0005961793,0.0001202302,0.00006577548,0.00001121115,0.0001739197,0.0001339684,0.00006062651],"category_scores_gemma":[0.0003796547,0.0001423846,0.0001442925,0.0004651457,0.00013309,0.00009732234,0.0002490287,0.0005048977,0.000005776542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000161895,"about_ca_system_score_gemma":0.001262664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001360246,"about_ca_topic_score_gemma":0.000006049069,"domain_scores_codex":[0.9962003,0.0001629827,0.001430327,0.0002041214,0.001712077,0.0002901752],"domain_scores_gemma":[0.9982346,0.0001829339,0.0003130132,0.00053443,0.0003872218,0.0003478119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001166681,0.007510903,0.8894998,0.00475775,0.0002885264,0.00006265163,0.07362776,0.003393118,0.0001439544,0.00002970752,0.001016157,0.01850295],"study_design_scores_gemma":[0.004360223,0.002296513,0.597618,0.0009698991,0.0005325219,0.0002141065,0.007673543,0.3649237,0.01970907,0.0000533358,0.001174917,0.0004741747],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902729,0.0001713034,0.00515094,0.00008712741,0.00005877843,0.001508575,0.00002265212,0.00003209333,0.002695629],"genre_scores_gemma":[0.990644,0.0000141999,0.00850145,0.000093626,0.00003441639,0.000410602,0.0001193303,0.00001469885,0.000167654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3615306,"threshold_uncertainty_score":0.5806276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03535267777232089,"score_gpt":0.2609526600505753,"score_spread":0.2255999822782544,"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."}}