{"id":"W4415700648","doi":"10.1148/ryct.250080","title":"Open-Source AI Model for Predicting Respiratory Mortality in COPD from Chest Radiographs","year":2025,"lang":"en","type":"article","venue":"Radiology Cardiothoracic Imaging","topic":"Chronic Obstructive Pulmonary Disease (COPD) Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Research Foundation of Korea","keywords":"COPD; Receiver operating characteristic; Pulmonary function testing; Retrospective cohort study; Radiography; Respiratory system; Cohort; Cohort study; Hazard ratio","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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.001301026,0.001094661,0.0007119882,0.001352614,0.0003243106,0.00114018,0.001228056,0.001027184,0.003109571],"category_scores_gemma":[0.00500105,0.0002583828,0.0008901398,0.0008342501,0.0002195126,0.0006283617,0.0006275752,0.001249806,0.001009888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007923646,"about_ca_system_score_gemma":0.001019168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01293586,"about_ca_topic_score_gemma":0.01046021,"domain_scores_codex":[0.9996336,0.00009678196,0.00004164525,0.000115205,0.00007155923,0.00004120343],"domain_scores_gemma":[0.9980488,0.001308372,0.0001067541,0.00008170529,0.0003686641,0.00008566611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001271853,0.0009805844,0.04086813,0.0003221286,0.0006026241,0.0005154004,0.0000985549,0.7282824,0.002188492,0.001870253,0.01213205,0.2108676],"study_design_scores_gemma":[0.00002274491,0.00005728698,0.002000737,0.00001691858,0.00003690237,0.00004528541,0.000009579012,0.9960907,0.0003243677,0.0009026157,0.0004837197,0.000009081186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5499423,0.003846534,0.4099195,0.002688909,0.001074006,0.0004285796,0.01280758,0.01082732,0.008465409],"genre_scores_gemma":[0.9248588,0.0005843517,0.06164726,0.0003076028,0.0002389792,0.0002889027,0.007926026,0.0001187087,0.004029336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.998772,"threshold_uncertainty_score":0.02572113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03018869262485638,"score_gpt":0.3662935654058549,"score_spread":0.3361048727809985,"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."}}