{"id":"W3159233357","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a4605","title":"Investigating Machine-Based Learning to Predict Patient Reported Outcomes from Spirometry and Oscillometry","year":2021,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Dalhousie University","funders":"","keywords":"Spirometry; Computer science; Artificial intelligence; Machine learning; Medical physics; Medicine; Internal medicine; Asthma","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":[0.000460039,0.0002435816,0.000353903,0.0002883085,0.000281101,0.0002794508,0.0003828356,0.0001025357,0.0001309121],"category_scores_gemma":[0.00491228,0.0002194273,0.00007096942,0.001354607,0.00003460286,0.0002091152,0.0007168733,0.0005943027,0.00002070037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000719323,"about_ca_system_score_gemma":0.0002117971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001362701,"about_ca_topic_score_gemma":0.00006019786,"domain_scores_codex":[0.9971163,0.0003457387,0.0005854563,0.0009145023,0.000638739,0.0003992837],"domain_scores_gemma":[0.9976325,0.0006715931,0.0002269807,0.0008167136,0.0001835141,0.0004686801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000001261641,0.00002401802,0.9577329,0.00003172502,0.00002144824,0.00008694771,0.0006168797,0.001435072,0.0006672525,0.0002628307,0.0001010945,0.03901854],"study_design_scores_gemma":[0.000423019,0.0001960955,0.8396541,0.00009931243,0.00001047813,0.000024648,0.0001330815,0.1540936,0.002451769,0.0003347528,0.00222129,0.0003578331],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8646049,0.0002254456,0.1269127,0.006135766,0.0003729977,0.0001801668,0.000005098786,0.000568753,0.0009941306],"genre_scores_gemma":[0.7359968,0.000001793382,0.2593792,0.004344695,0.00002847726,0.00001303189,0.00002026273,0.00001835395,0.000197395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1526586,"threshold_uncertainty_score":0.8947985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02644911701375424,"score_gpt":0.2842186118753558,"score_spread":0.2577694948616016,"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."}}