{"id":"W4240573104","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a4568","title":"Radiomics Analysis to Predict Presence of Chronic Obstructive Pulmonary Disease and Symptoms Using Machine Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Paul's Hospital; Toronto Metropolitan University","funders":"","keywords":"Radiomics; Pulmonary disease; Medicine; Computer science; Disease; Artificial intelligence; Machine learning; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007738003,0.0004898621,0.0006162508,0.0023586,0.0001886893,0.0009896896,0.0003050594,0.0006470812,0.001469268],"category_scores_gemma":[0.002133933,0.0001315131,0.0006096602,0.0008315947,0.0002541736,0.000371661,0.0003150336,0.0003675701,0.0005772084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001942746,"about_ca_system_score_gemma":0.0002409576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001313582,"about_ca_topic_score_gemma":0.001217552,"domain_scores_codex":[0.9995517,0.0001371852,0.00005253491,0.00008586862,0.000118799,0.00005383543],"domain_scores_gemma":[0.9989387,0.0005073384,0.0001906045,0.00006192226,0.0002374499,0.00006388391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001877859,0.0004661815,0.7503461,0.0002223248,0.0006441833,0.0006323903,0.0001151892,0.01109794,0.03884979,0.0006677895,0.002394879,0.1926853],"study_design_scores_gemma":[0.00012024,0.001067953,0.6966546,0.00009499199,0.0008081037,0.002808365,0.0003171785,0.2754921,0.01723948,0.001622388,0.00368401,0.00009061061],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9367039,0.003356534,0.05156574,0.0003521254,0.0001638968,0.0001271334,0.001575688,0.00068031,0.005474536],"genre_scores_gemma":[0.9916928,0.0003023005,0.006646531,0.00007979181,0.0001117845,0.00003366221,0.0005619856,0.00002120692,0.0005499833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0023586,"threshold_uncertainty_score":0.004915118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008262739076586189,"score_gpt":0.2722305324846669,"score_spread":0.2639677934080807,"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."}}