{"id":"W2162923601","doi":"10.1002/cjs.10044","title":"Nonparametric covariate adjustment for receiver operating characteristic curves","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Covariate; Nonparametric statistics; Receiver operating characteristic; Estimator; Statistics; Mathematics; Consistency (knowledge bases); Asymptotic distribution; Econometrics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0007423791,0.0001708977,0.000457912,0.0002365778,0.0001492842,0.00008405987,0.0002288807,0.00006623856,0.0003366419],"category_scores_gemma":[0.01086692,0.0001496898,0.00006786783,0.0002316316,0.00006503951,0.00009277322,0.000004549288,0.0002324238,0.000006095429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001637783,"about_ca_system_score_gemma":0.0008548038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000156355,"about_ca_topic_score_gemma":0.0002760521,"domain_scores_codex":[0.9984332,0.0001029158,0.000730774,0.0001374029,0.000206503,0.0003891901],"domain_scores_gemma":[0.9962763,0.001854352,0.0004363218,0.0001541638,0.0006877999,0.0005911094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002227853,0.00005090514,0.0001563429,0.0002885212,0.00006285358,0.0001698941,0.0002323604,0.00000276482,0.0001027307,0.7689952,0.05755496,0.1723612],"study_design_scores_gemma":[0.0008051053,0.00110214,0.01884565,0.0008469117,0.0003022216,0.0001703687,0.00006106495,0.001712743,0.00008805034,0.9721728,0.003538147,0.0003547897],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005427343,0.0004430751,0.9960507,0.0005212196,0.0005885822,0.000273678,0.001191372,0.000006106456,0.0003825096],"genre_scores_gemma":[0.02700769,0.0001483827,0.9714789,0.0009716228,0.0002509227,0.000004481004,0.00001530919,0.00001915217,0.0001035616],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2031776,"threshold_uncertainty_score":0.997465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08102564128087644,"score_gpt":0.3487683950876566,"score_spread":0.2677427538067801,"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."}}