{"id":"W3198875596","doi":"10.1183/13993003.01186-2021","title":"Moving beyond AUC: decision curve analysis for quantifying net benefit of risk prediction models","year":2021,"lang":"en","type":"article","venue":"European Respiratory Journal","topic":"Clinical practice guidelines implementation","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of Toronto; Centre for Advancing Health Outcomes; Ottawa Hospital; University of British Columbia","funders":"","keywords":"Medicine; Receiver operating characteristic; Risk model; Area under the curve; Statistics; Machine learning; Econometrics; Risk analysis (engineering); Internal medicine; Computer science; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005793038,0.0001603102,0.0004464567,0.0003967139,0.0002620868,0.00008675974,0.0001287917,0.00005780994,0.0002188277],"category_scores_gemma":[0.007751201,0.0001481181,0.0005032744,0.000654086,0.00003513758,0.0004746182,0.00009212231,0.0005188039,0.00001081778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009628722,"about_ca_system_score_gemma":0.0002151182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001026836,"about_ca_topic_score_gemma":0.00005848311,"domain_scores_codex":[0.9962465,0.0005753061,0.001987573,0.0003461272,0.0006036291,0.0002408194],"domain_scores_gemma":[0.9952741,0.001586582,0.001219353,0.0004168102,0.001267134,0.0002360501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002154249,0.0005714719,0.5004521,0.0001081057,0.002680557,0.0004287639,0.0009230339,0.1380169,0.01601225,0.0003733512,0.006137245,0.332142],"study_design_scores_gemma":[0.01134817,0.001385597,0.8194446,0.0004991731,0.009198267,0.0003558299,0.002352162,0.1231384,0.002749442,0.003442846,0.02561096,0.0004745291],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6633103,0.0007252654,0.3347159,0.000128889,0.0003295863,0.0001480916,0.0001005843,0.00002266013,0.0005186868],"genre_scores_gemma":[0.9668636,0.0003017531,0.03094163,0.0008049731,0.0009108979,0.000002091439,0.00007374748,0.00005376742,0.00004758549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3316675,"threshold_uncertainty_score":0.9279472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2792227222109293,"score_gpt":0.4475421732569788,"score_spread":0.1683194510460495,"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."}}