{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00207878,0.0003032555,0.0003429384,0.0006173928,0.0003479558,0.0004744577,0.000330756,0.0004176306,0.0007455173],"category_scores_gemma":[0.002979875,0.0002879117,0.0007471403,0.0006562621,0.0001987386,0.0005394318,0.0003230156,0.0006856472,0.0001879112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002053389,"about_ca_system_score_gemma":0.0003675759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00226648,"about_ca_topic_score_gemma":0.002005854,"domain_scores_codex":[0.9994023,0.0001518515,0.00008290053,0.0001588503,0.0001492343,0.00005486517],"domain_scores_gemma":[0.9983992,0.0003638875,0.000562973,0.0003204712,0.000230222,0.0001232443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001445466,0.00002825011,0.998913,0.000004485821,0.00005100732,0.0000341915,0.00001844477,0.00004814487,0.00009698189,0.00001235631,0.00002548108,0.0006229886],"study_design_scores_gemma":[0.00001984587,0.0003308879,0.9977236,0.00001016676,0.0000895773,0.0003776863,0.0000790949,0.001036145,0.0001292472,0.00003258368,0.0001646464,0.000006525765],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989373,0.0001777845,0.000431665,0.00001564526,0.000005597235,0.00001831802,0.0002855728,0.000003611077,0.000124531],"genre_scores_gemma":[0.9990208,0.00009562277,0.0003110268,0.00001451817,0.000007232396,0.00001988836,0.0004534553,0.000002276751,0.00007520585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00226648,"threshold_uncertainty_score":0.01099378,"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."}}