{"id":"W2003011786","doi":"10.1016/j.trsl.2006.05.006","title":"Mixture models of serum iron measures in population screening for hemochromatosis and iron overload","year":2006,"lang":"en","type":"article","venue":"Translational research","topic":"Iron Metabolism and Disorders","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"National Center for Research Resources; National Heart, Lung, and Blood Institute","keywords":"Hemochromatosis; Transferrin saturation; Hereditary hemochromatosis; Genotype; Genetics; Serum iron; Population; Trait; Biology; Medicine; Internal medicine; Ferritin; Gene; Serum ferritin; Anemia; Computer science; Environmental health","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":[0.02187285,0.002011119,0.003642547,0.002549287,0.0009520049,0.002887778,0.003991961,0.003485959,0.002893535],"category_scores_gemma":[0.04298997,0.002074111,0.00384827,0.001556494,0.002256674,0.002999756,0.002180415,0.003660932,0.0009530054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00160202,"about_ca_system_score_gemma":0.001120767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01645352,"about_ca_topic_score_gemma":0.012703,"domain_scores_codex":[0.9933546,0.004862438,0.0002048202,0.0009739381,0.0002576496,0.000346582],"domain_scores_gemma":[0.9217535,0.07153301,0.002238639,0.002627403,0.001237121,0.0006103973],"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":[0.003958802,0.0007418381,0.05888376,0.0002163281,0.002394668,0.0005007195,0.0008357849,0.8409055,0.001172222,0.03748469,0.003104105,0.04980151],"study_design_scores_gemma":[0.0001292445,0.0001292343,0.007745495,0.00002665605,0.0002439542,0.0001185983,0.00004425415,0.9728626,0.0001593729,0.01813867,0.0003511996,0.00005081424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5041301,0.001766026,0.48677,0.002319648,0.0002421387,0.0002616314,0.00210803,0.00096354,0.001438977],"genre_scores_gemma":[0.9601998,0.0005944242,0.03017434,0.0002976025,0.0002456506,0.0004822667,0.002164831,0.0001258077,0.00571532],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02187285,"threshold_uncertainty_score":0.115676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07883667875809065,"score_gpt":0.3647877074660811,"score_spread":0.2859510287079905,"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."}}