{"id":"W2060277785","doi":"10.1158/1078-0432.ovca13-a20","title":"Abstract A20: Integrating high-throughput technologies for the identification and validation of ovarian cancer biomarkers","year":2013,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Biological Research and Disease Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Mount Sinai Hospital; University of Toronto","funders":"","keywords":"Ovarian cancer; Cancer; Medicine; Biomarker discovery; Malignancy; Identification (biology); Biomarker; Proteomics; Bioinformatics; Computational biology; Oncology; Biology; Internal medicine; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001852308,0.00008652226,0.0001446149,0.00003887349,0.000235192,0.00005889002,0.000293435,0.0001766562,0.00006683869],"category_scores_gemma":[0.005229502,0.00005056461,0.00007834989,0.0001647019,0.001087763,0.00000954384,0.0002780777,0.0002261343,0.000004129068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001954982,"about_ca_system_score_gemma":0.0001577811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001480229,"about_ca_topic_score_gemma":0.000187692,"domain_scores_codex":[0.9985915,0.0001304648,0.0003829943,0.0003724631,0.0002228389,0.0002997777],"domain_scores_gemma":[0.998123,0.0007569471,0.0001046426,0.0002947306,0.000657059,0.00006358612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00064009,0.0001335752,0.03383854,0.0001697095,0.0003805384,3.446848e-7,0.00002862372,0.000008134717,0.4329266,0.0009042172,0.02734321,0.5036263],"study_design_scores_gemma":[0.001534016,0.0008742254,0.5418066,0.0001218223,0.00004570534,4.084857e-7,0.002359893,0.0002802649,0.4237465,0.01200719,0.01694389,0.000279514],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782509,0.009739083,0.000692928,0.009834011,0.0001354997,0.001081626,0.0001119571,0.00001733732,0.0001366141],"genre_scores_gemma":[0.9852424,0.01299182,0.000224652,0.00004677152,0.0001663948,0.001159178,0.00003167344,0.000008253349,0.000128885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5079681,"threshold_uncertainty_score":0.626058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1586183839477584,"score_gpt":0.5001702391840552,"score_spread":0.3415518552362968,"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."}}