{"id":"W4391878999","doi":"10.2196/53654","title":"Development of Cost-Effective Fatty Liver Disease Prediction Models in a Chinese Population: Statistical and Machine Learning Approaches","year":2024,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nonalcoholic fatty liver disease; Support vector machine; Artificial intelligence; Machine learning; Random forest; Steatosis; Computer science; Fatty liver; Transient elastography; Gradient boosting; Receiver operating characteristic; Logistic regression; Medicine; Ensemble learning; Liver biopsy; Radiology; Disease; Pathology; Internal medicine; Biopsy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004220841,0.001047613,0.001066723,0.00204586,0.0004509479,0.001128144,0.001462337,0.0006912714,0.001619459],"category_scores_gemma":[0.008991095,0.0004146292,0.001247354,0.001499493,0.0002753442,0.0009400218,0.0009446137,0.0009541934,0.0002931964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001943054,"about_ca_system_score_gemma":0.003332938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03596817,"about_ca_topic_score_gemma":0.02076328,"domain_scores_codex":[0.9993692,0.0002729025,0.00006197293,0.0001254044,0.00009959115,0.00007095345],"domain_scores_gemma":[0.9970811,0.002023911,0.0002251946,0.0001005283,0.0004658053,0.0001033627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003675607,0.0004435868,0.1186485,0.0002586663,0.0005251946,0.0005584201,0.000167778,0.6769007,0.001013623,0.003672666,0.003572464,0.1938709],"study_design_scores_gemma":[0.00001130961,0.00004422884,0.003868106,0.00001413709,0.0000518796,0.00002795102,0.00003314666,0.994172,0.0001654226,0.001336295,0.0002660882,0.000009422208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4723535,0.00224405,0.5163755,0.003437331,0.0001467153,0.0004691086,0.001661947,0.001021307,0.002290421],"genre_scores_gemma":[0.8789698,0.00108377,0.116184,0.000242959,0.0001098819,0.000540236,0.001299674,0.00005803914,0.001511547],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03596817,"threshold_uncertainty_score":0.07151765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1106899039930845,"score_gpt":0.3922079632379021,"score_spread":0.2815180592448175,"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."}}