{"id":"W3205747317","doi":"10.2147/rmhp.s328180","title":"Machine Learning-Based Prediction for 4-Year Risk of Metabolic Syndrome in Adults: A Retrospective Cohort Study","year":2021,"lang":"en","type":"article","venue":"Risk Management and Healthcare Policy","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nipissing University","funders":"National Office for Philosophy and Social Sciences; National Natural Science Foundation of China","keywords":"Metabolic syndrome; Retrospective cohort study; Medicine; Cohort; Artificial intelligence; Machine learning; Computer science; Internal medicine; Obesity","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002304473,0.0002626072,0.0007645641,0.0006226526,0.0008976196,0.00001164654,0.0001547483,0.0002155886,0.00008066377],"category_scores_gemma":[0.001108421,0.0002627168,0.0001076881,0.001054388,0.00006905955,0.0001044348,0.0001629091,0.001094004,0.0000238741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000371932,"about_ca_system_score_gemma":0.0003887559,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1096079,"about_ca_topic_score_gemma":0.062008,"domain_scores_codex":[0.9949383,0.001940322,0.001225319,0.0007235007,0.0004294152,0.0007431763],"domain_scores_gemma":[0.9975258,0.0004741101,0.0007032623,0.000508338,0.0005718771,0.000216683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003483368,0.0002570789,0.9740057,0.001329721,0.000129308,0.00001562514,0.00403985,0.0001613529,1.485855e-7,0.01452285,0.00005858353,0.005131469],"study_design_scores_gemma":[0.001452392,0.0007392654,0.9696587,0.0004723418,0.0001421074,7.64608e-7,0.01337045,0.007785405,0.000004129364,0.004392449,0.001811848,0.0001701043],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9848417,0.002787242,0.000444455,0.002342966,0.0003708217,0.007424706,0.0008902682,0.0001236435,0.0007742077],"genre_scores_gemma":[0.9865071,0.01030023,0.0005474048,0.0002875626,0.0002239357,0.001370189,0.0001252414,0.00004987951,0.0005884874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0475999,"threshold_uncertainty_score":0.9999825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0387149856799902,"score_gpt":0.4036399559829319,"score_spread":0.3649249703029417,"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."}}