{"id":"W1968246914","doi":"10.1118/1.3476104","title":"Sci—Thur PM: YIS — 09: ROC Analysis of Metabolomics Data Sets for Cancer Screening","year":2010,"lang":"en","type":"article","venue":"Medical Physics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services; University of Alberta","funders":"","keywords":"Metabolite; Metabolomics; Urine; Area under the curve; Population; Multivariate analysis; Receiver operating characteristic; Multivariate statistics; Chemistry; Internal medicine; Biology; Medicine; Chromatography; Mathematics; Statistics","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":[0.008237815,0.001703845,0.001502273,0.00612562,0.0004794734,0.00210745,0.001387044,0.001404239,0.04822367],"category_scores_gemma":[0.0304534,0.0007712594,0.001914217,0.003628061,0.0008272877,0.002085005,0.002359947,0.002147359,0.03007898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007870586,"about_ca_system_score_gemma":0.001149497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001496514,"about_ca_topic_score_gemma":0.001357961,"domain_scores_codex":[0.9969589,0.001271798,0.0003050716,0.0004664423,0.0008682912,0.000129576],"domain_scores_gemma":[0.9901648,0.005204991,0.0005769592,0.001741174,0.001822851,0.000489241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001640852,0.0004720507,0.01193489,0.0008986612,0.001031047,0.0005325635,0.0002351142,0.01513088,0.008347305,0.009271321,0.5403391,0.4101662],"study_design_scores_gemma":[0.000432719,0.000870954,0.0374687,0.0003401845,0.000273745,0.001261024,0.0002151832,0.7409922,0.01975676,0.02928534,0.1688082,0.0002949827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02385626,0.001861484,0.6996754,0.005447481,0.001650491,0.0008172496,0.03995826,0.2147902,0.01194325],"genre_scores_gemma":[0.2633381,0.001942329,0.6129112,0.002137925,0.001453689,0.003694856,0.06685989,0.02559254,0.02206955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04822367,"threshold_uncertainty_score":0.1613243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03430622012282383,"score_gpt":0.3370231391091349,"score_spread":0.3027169189863111,"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."}}