{"id":"W4410194123","doi":"10.1038/s41598-026-50260-4","title":"Machine Learning-based Mortality Prediction for Pediatric Fulminant Myocarditis Using Cytokine Profiles","year":2025,"lang":"en","type":"preprint","venue":"Scientific Reports","topic":"Viral Infections and Immunology Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"Fujita Health University; Japan Society for the Promotion of Science; Hospital for Sick Children; Japan Agency for Medical Research and Development; Suzuken Memorial Foundation","keywords":"Fulminant; Myocarditis; Cytokine; Medicine; Computer science; Internal medicine; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001916009,0.0007585475,0.000732547,0.001314163,0.0001806663,0.0006091708,0.0003624964,0.0003734742,0.0004314396],"category_scores_gemma":[0.003377598,0.0001739719,0.0005965485,0.0005455166,0.0001750809,0.0003358384,0.0004495434,0.0007892773,0.0001998148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002695084,"about_ca_system_score_gemma":0.0005770926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001413198,"about_ca_topic_score_gemma":0.001001652,"domain_scores_codex":[0.9995456,0.000230514,0.00004806347,0.00007817693,0.00004959608,0.00004808754],"domain_scores_gemma":[0.9986438,0.0007982287,0.0002143364,0.00006244455,0.0001752477,0.0001059674],"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":[0.001249203,0.000370512,0.7525083,0.00007493543,0.0002674507,0.0003603716,0.000101701,0.1345459,0.004797853,0.0001797892,0.000794988,0.1047489],"study_design_scores_gemma":[0.00002861916,0.0003291934,0.08059487,0.00002053658,0.00006876828,0.0001911511,0.0000545327,0.9168132,0.001254456,0.0004491103,0.000179026,0.00001647811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9671127,0.0006422664,0.03121815,0.0002060906,0.00002593201,0.00004676695,0.0003214875,0.0001283423,0.0002982629],"genre_scores_gemma":[0.9914647,0.0001487629,0.007750694,0.0000207044,0.00002106915,0.00003188609,0.0004572912,0.000006090595,0.0000989393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001916009,"threshold_uncertainty_score":0.01013291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05361490450914507,"score_gpt":0.3536653512870661,"score_spread":0.300050446777921,"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."}}