{"id":"W3020235507","doi":"10.1016/j.chest.2020.02.079","title":"Machine Learning and Prediction of All-Cause Mortality in COPD","year":2020,"lang":"en","type":"article","venue":"CHEST Journal","topic":"Chronic Obstructive Pulmonary Disease (COPD) Research","field":"Medicine","cited_by":127,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences Centre","funders":"National Heart, Lung, and Blood Institute; Sunovion; COPD Foundation; GlaxoSmithKline; National Institutes of Health; Pfizer","keywords":"COPD; Medicine; BODE index; Internal medicine; Cardiology; Mortality rate; Physical therapy; Pulmonary rehabilitation","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":[0.0002991412,0.00009300922,0.0002260564,0.00008336361,0.00004441028,0.00001545036,0.00005498228,0.00005302735,0.0003502236],"category_scores_gemma":[0.0002915435,0.00008266907,0.00005330937,0.0001646441,0.0001086472,0.0001123293,0.00005849703,0.0007358157,0.000005383843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001293888,"about_ca_system_score_gemma":0.0001574958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002508192,"about_ca_topic_score_gemma":0.000006286445,"domain_scores_codex":[0.9989238,0.00008711475,0.0002890691,0.0001604774,0.0003462536,0.0001933152],"domain_scores_gemma":[0.9993394,0.0000378613,0.00009690101,0.00007605165,0.00008176074,0.0003680293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000237168,0.0001021002,0.9872833,0.0004888207,0.0001775193,0.0004284614,0.0004490866,0.00003084404,0.00747165,0.00002302204,0.0001267385,0.003181265],"study_design_scores_gemma":[0.002132917,0.0001882725,0.983479,0.0001044166,0.000133516,0.0003095415,0.0003276842,0.009193211,0.0005514467,0.00007057996,0.003453188,0.00005618534],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994416,0.002588028,0.00008703598,0.001562422,0.00004599902,0.0001545614,0.00001963956,0.00001599837,0.001110294],"genre_scores_gemma":[0.9984204,0.0009903263,0.0001474323,0.0000806927,0.0002376481,0.000002354189,0.00001806485,0.00001508883,0.00008801457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009162366,"threshold_uncertainty_score":0.3834703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06160860739882797,"score_gpt":0.3336751738416583,"score_spread":0.2720665664428304,"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."}}