{"id":"W7053292768","doi":"","title":"Using Multilevel Outcomes to Construct and Select Biomarker Combinations for Single-level Prediction","year":2017,"lang":"en","type":"article","venue":"Collection of Biostatistics Research Archive","topic":"Magnetic confinement fusion research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Abbott Diagnostics; Pritzker School of Medicine; University of California, San Francisco; National Institutes of Health; Institute for Clinical Evaluative Sciences; U.S. Department of Veterans Affairs","keywords":"Outcome (game theory); Logistic regression; Biomarker; Context (archaeology); Construct (python library); Selection (genetic algorithm); Regression","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006005944,0.0001263355,0.0002094451,0.0005657964,0.00149682,0.0002002231,0.0002345649,0.00003143877,0.001184039],"category_scores_gemma":[0.001289994,0.0001269153,0.00004965482,0.0002069491,0.0004955999,0.0000496733,0.0002874536,0.0001651129,0.000003402692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003196624,"about_ca_system_score_gemma":0.0002958808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001886865,"about_ca_topic_score_gemma":0.0002517534,"domain_scores_codex":[0.9983886,0.0001478757,0.0003083726,0.0003164216,0.0004639712,0.0003746953],"domain_scores_gemma":[0.9971109,0.00130861,0.0001386809,0.0003334052,0.0009129717,0.0001954357],"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":[0.0005365806,0.0009084518,0.126688,0.0002062263,0.0003738659,0.000002821364,0.0009148678,0.00002085481,0.3332396,0.05836316,0.03797207,0.4407735],"study_design_scores_gemma":[0.00628254,0.00176262,0.6464623,0.0001739392,0.00006670239,0.000005442222,0.001164892,0.2783843,0.02399322,0.03270414,0.00856528,0.000434673],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0997522,0.000008530471,0.8545695,0.001204492,0.0002798565,0.003441192,0.008433714,0.00002107359,0.03228944],"genre_scores_gemma":[0.9271734,0.000004069097,0.07003137,0.000005935136,0.00003601805,0.0001576888,0.00008655433,0.0000164817,0.0024885],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8274212,"threshold_uncertainty_score":0.9998031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.204276076903986,"score_gpt":0.4232211065717832,"score_spread":0.2189450296677973,"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."}}