{"id":"W2169943027","doi":"10.1158/0008-5472.can-05-0669","title":"Application of Bayesian Modeling of Autologous Antibody Responses against Ovarian Tumor-Associated Antigens to Cancer Detection","year":2006,"lang":"en","type":"article","venue":"Cancer Research","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"National Cancer Institute","keywords":"Antibody; Ovarian cancer; Medicine; Antigen; Oncology; Confidence interval; Receiver operating characteristic; Internal medicine; Multiplex; Immunology; Cancer; Gastroenterology; Biology; Bioinformatics","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":[0.00426787,0.0008400349,0.001121466,0.0009156007,0.0004269889,0.001192582,0.001013753,0.0009122773,0.0009709927],"category_scores_gemma":[0.01102411,0.0007538175,0.001382641,0.0004521275,0.0006285227,0.0005869041,0.0006059262,0.0009444278,0.0002016359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001320893,"about_ca_system_score_gemma":0.00164931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01824452,"about_ca_topic_score_gemma":0.01137637,"domain_scores_codex":[0.9988487,0.000716646,0.00003793697,0.0001610724,0.0001280641,0.0001074248],"domain_scores_gemma":[0.9945511,0.004435018,0.0004950676,0.0001017628,0.0003044913,0.0001125965],"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":[0.0001148125,0.00004500287,0.005789656,0.00002677082,0.00008190542,0.00004835135,0.0000476357,0.9832045,0.0008736011,0.003724666,0.0001499865,0.005893027],"study_design_scores_gemma":[0.000009972616,0.0000221455,0.000637602,0.000004262813,0.00001066238,0.00001426303,0.000003555832,0.9972624,0.000120144,0.001831826,0.00007719109,0.00000593114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3891281,0.000694208,0.6058984,0.0007026098,0.00004008855,0.0002006994,0.0003774595,0.0003548613,0.00260358],"genre_scores_gemma":[0.9458575,0.0004124823,0.05087568,0.0001766353,0.00004329257,0.000287432,0.0004258209,0.00003587591,0.001885265],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01824452,"threshold_uncertainty_score":0.03627664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05233663540949336,"score_gpt":0.4195398334631923,"score_spread":0.367203198053699,"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."}}