{"id":"W2770017713","doi":"10.1186/s41512-018-0028-3","title":"Using ordinal outcomes to construct and select biomarker combinations for single-level prediction","year":2018,"lang":"en","type":"article","venue":"Diagnostic and Prognostic Research","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Abbott Diagnostics; Pritzker School of Medicine; University of California, San Francisco; National Institutes of Health; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; Institute for Clinical Evaluative Sciences; U.S. Department of Veterans Affairs","keywords":"Outcome (game theory); Logistic regression; Biomarker; Context (archaeology); Computer science; Selection (genetic algorithm); Construct (python library); Machine learning; Artificial intelligence; Data mining; Statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0006873662,0.0001746752,0.000307905,0.0003547304,0.0004980561,0.0001162777,0.00005603272,0.0000974736,0.00005361329],"category_scores_gemma":[0.0107312,0.0001375658,0.00004858764,0.0004306802,0.0004502972,0.0000949478,0.000089099,0.0001269527,0.00001572501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009022616,"about_ca_system_score_gemma":0.000143604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001395301,"about_ca_topic_score_gemma":0.000023866,"domain_scores_codex":[0.9982858,0.0001050258,0.0002683931,0.000449085,0.0004073402,0.0004843112],"domain_scores_gemma":[0.9912817,0.007363431,0.00003647369,0.0001908431,0.0007757369,0.0003518102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001230895,0.0006170222,0.9653718,0.00009775873,0.0001816687,0.00002362771,0.0002435466,2.716247e-7,0.0008435982,0.001805344,0.002075594,0.02861666],"study_design_scores_gemma":[0.003041529,0.003564975,0.9861693,0.0003956635,0.0002705862,0.0002018769,0.0002666043,0.001192165,0.00169355,0.001530196,0.001520566,0.0001529884],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906757,0.0005208296,0.002465219,0.002656071,0.0001603055,0.002606846,0.000153475,0.00004106133,0.0007204154],"genre_scores_gemma":[0.9929977,0.0000935645,0.00597143,0.0002163677,0.0001243219,0.0004363155,0.00005322511,0.00002712876,0.00007995174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02846367,"threshold_uncertainty_score":0.9976018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5615408223281894,"score_gpt":0.5013232235527673,"score_spread":0.0602175987754221,"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."}}