{"id":"W3048678791","doi":"10.2196/18331","title":"Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Gout, Hyperuricemia, Uric Acid","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Machine learning; Decision tree; Medicine; Artificial intelligence; Uric acid; Population; Computer science; Linear regression; Environmental health; Internal medicine","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":[0.0002379869,0.0002428571,0.0004339648,0.00008279602,0.0001431851,0.00002945344,0.0001394795,0.0002156303,0.00008588059],"category_scores_gemma":[0.0001312611,0.0001735682,0.00003018587,0.0002933297,0.0001323957,0.000284144,0.0001350062,0.0009939546,0.00006282643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004845428,"about_ca_system_score_gemma":0.0003640969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001396837,"about_ca_topic_score_gemma":7.82543e-7,"domain_scores_codex":[0.9975469,0.00001700164,0.0007358772,0.0001564491,0.001211641,0.0003322044],"domain_scores_gemma":[0.9987493,0.00002965728,0.0002017568,0.0001699965,0.0000776617,0.0007716729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00189987,0.001427438,0.6182048,0.0076371,0.001209217,0.0001480911,0.1691256,0.003407162,0.0004138927,0.0001248266,0.007299413,0.1891026],"study_design_scores_gemma":[0.003586331,0.001191681,0.004461057,0.0002351141,0.0001942556,0.0003228662,0.001071062,0.9754729,0.001565028,0.000001212793,0.01168559,0.0002129493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838659,0.0001837086,0.01144275,0.001126828,0.00002940833,0.000465681,0.000003899451,0.0002611311,0.002620744],"genre_scores_gemma":[0.9767443,0.0001175788,0.02052317,0.002156032,0.0001021001,0.00005390228,0.0001147396,0.00002569963,0.0001625027],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9720657,"threshold_uncertainty_score":0.7077907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02246432107523374,"score_gpt":0.2540455790390092,"score_spread":0.2315812579637754,"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."}}