{"id":"W3002890430","doi":"10.29219/fnr.v64.3712","title":"Prediction model of artificial neural network for the risk of hyperuricemia incorporating dietary risk factors in a Chinese adult study","year":2020,"lang":"en","type":"article","venue":"Food & Nutrition Research","topic":"Gout, Hyperuricemia, Uric Acid","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Impact","funders":"","keywords":"Medicine; Logistic regression; Receiver operating characteristic; Framingham Risk Score; Artificial neural network; Hyperuricemia; Odds ratio; Predictive modelling; Odds; Statistics; Demography; Machine learning; Internal medicine; Mathematics; Computer science; Uric acid; Disease","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":[0.005140964,0.00101212,0.0008333967,0.0009982921,0.0003956055,0.0008188737,0.0009920265,0.0007262048,0.002038702],"category_scores_gemma":[0.006587856,0.0003064654,0.001304809,0.0005414382,0.0002208252,0.0004247676,0.0005276144,0.0008538183,0.0002685143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008783893,"about_ca_system_score_gemma":0.001342409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02489303,"about_ca_topic_score_gemma":0.008167709,"domain_scores_codex":[0.999302,0.0003270977,0.0000662819,0.0001336941,0.00008187096,0.00008904374],"domain_scores_gemma":[0.9977528,0.001370101,0.0002069313,0.00009548009,0.0004355289,0.0001390831],"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":[0.001541125,0.000638204,0.876953,0.0001321281,0.001014956,0.0005919544,0.0002563388,0.09572214,0.0004498783,0.0004670126,0.001878253,0.02035497],"study_design_scores_gemma":[0.00007397778,0.0003277232,0.07515431,0.00003616339,0.0002721974,0.00009212163,0.00009787722,0.9232252,0.0001236609,0.0003942293,0.0001848911,0.0000176083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871119,0.0004509506,0.01045963,0.0004630903,0.0000761936,0.0001223649,0.0005231251,0.00005695654,0.0007357257],"genre_scores_gemma":[0.9951728,0.0002087158,0.003034587,0.00005376526,0.00002434075,0.0002217951,0.0006808892,0.000006212964,0.0005969874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02489303,"threshold_uncertainty_score":0.04949629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1238011064102637,"score_gpt":0.3486232614469246,"score_spread":0.2248221550366609,"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."}}