{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005138892,0.0001280201,0.0002153957,0.0003395265,0.0001102248,0.00003673272,0.00003345275,0.0000423697,0.000042144],"category_scores_gemma":[0.00006578496,0.00008930563,0.00003832787,0.0003580035,0.00009609935,0.0003186702,0.00009583947,0.0003449082,0.0000120894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003159894,"about_ca_system_score_gemma":0.0001349144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007995316,"about_ca_topic_score_gemma":0.00003038319,"domain_scores_codex":[0.998555,0.000204974,0.0002560775,0.0002488935,0.0005051279,0.0002299385],"domain_scores_gemma":[0.9992264,0.0003443911,0.00002476399,0.00009241829,0.00008754399,0.0002244865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001348675,0.001903916,0.6798148,0.003407988,0.000346114,0.0002267832,0.03295784,0.0005019177,0.00002164008,0.006872528,0.00007970716,0.2725181],"study_design_scores_gemma":[0.0007349785,0.000185592,0.7340161,0.0003874736,0.00002294303,0.0000035915,0.0002690748,0.2636398,0.0000348379,0.0005785374,0.00007336371,0.00005374988],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908525,0.002240209,0.00312709,0.0001217978,0.00002359555,0.002951439,0.000191961,0.00003926681,0.0004520947],"genre_scores_gemma":[0.99657,0.000174732,0.0006414474,0.00000418926,0.00002395386,0.002034739,0.0005116164,0.00001395426,0.00002536771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2724643,"threshold_uncertainty_score":0.3641778,"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."}}