{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002216099,0.0001596586,0.0004651719,0.0002635556,0.00009531715,0.00002460369,0.00008185716,0.00004796884,0.0001766031],"category_scores_gemma":[0.0008367889,0.0001407487,0.00014894,0.0008762914,0.0001307628,0.00008155293,0.0001634695,0.0003564947,0.000001202079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001028844,"about_ca_system_score_gemma":0.000217836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00019048,"about_ca_topic_score_gemma":0.00001020807,"domain_scores_codex":[0.9985924,0.00009407335,0.0003082466,0.0004210406,0.0003246173,0.0002595752],"domain_scores_gemma":[0.9988444,0.0001438304,0.00009314869,0.0002935307,0.0001294357,0.0004955903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001656786,0.0001480308,0.8952625,0.0002619928,0.001293341,0.0004887326,0.0002926923,0.03274286,0.04023919,0.0002737943,0.00001476579,0.02881644],"study_design_scores_gemma":[0.000408385,0.00007982124,0.1557812,0.000100631,0.001644519,0.0001241509,0.00009517766,0.8402517,0.0009246257,0.00005257199,0.000407949,0.0001291869],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9681962,0.002739543,0.02754684,0.0006808212,0.00008399464,0.0001790033,0.0000119332,0.00004908431,0.0005125604],"genre_scores_gemma":[0.9904323,0.0001721168,0.008036662,0.0001878621,0.0001358594,0.000004578155,0.00005969771,0.00002430152,0.0009465751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8075089,"threshold_uncertainty_score":0.5739567,"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."}}