{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01423614,0.001023659,0.001017954,0.001256096,0.0002881921,0.002116755,0.001340992,0.001307133,0.001093098],"category_scores_gemma":[0.06426006,0.0003080572,0.001494633,0.000921668,0.000579139,0.00197891,0.0007482272,0.001630501,0.0003496512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007352515,"about_ca_system_score_gemma":0.001261332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004300233,"about_ca_topic_score_gemma":0.002996645,"domain_scores_codex":[0.9946195,0.0036519,0.0002675204,0.0006011723,0.0005829334,0.0002770349],"domain_scores_gemma":[0.9295447,0.06387509,0.002610175,0.001211335,0.002337021,0.0004216291],"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":[0.001884257,0.001323198,0.8927161,0.0001786564,0.001386175,0.0001231389,0.0002217012,0.04520033,0.0009252492,0.001205862,0.0005953897,0.05423987],"study_design_scores_gemma":[0.0001097618,0.001948923,0.2150224,0.0001030286,0.0006185574,0.0002125462,0.0004053897,0.7746971,0.001948767,0.004305436,0.0005768598,0.00005121108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9799868,0.0009761247,0.01652364,0.0008412093,0.00007776354,0.0000634259,0.0003925358,0.00009495838,0.001043463],"genre_scores_gemma":[0.9948307,0.0001636698,0.004150948,0.00008848358,0.00006108351,0.00002791294,0.0003998615,0.000009326669,0.000268099],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01423614,"threshold_uncertainty_score":0.07528877,"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."}}