{"id":"W4412835675","doi":"10.1101/2025.07.30.25332459","title":"Light Convolutional Neural Network to Detect Chronic Obstructive Pulmonary Disease (COPDxNet): A Multicenter Model Development and External Validation Study","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Chronic Obstructive Pulmonary Disease (COPD) Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"Mylan; COPD Foundation; Brown University; American College of Radiology Imaging Network; National Heart, Lung, and Blood Institute; Division of Cancer Prevention, National Cancer Institute; AstraZeneca; National Cancer Institute; National Institutes of Health; U.S. Department of Health and Human Services","keywords":"Medicine; COPD; Receiver operating characteristic; Gold standard (test); Nuclear medicine; Radiology; Area under the curve; Convolutional neural network; Pulmonary disease; Internal medicine; Artificial intelligence; Computer science","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":[0.02183428,0.002006286,0.001113109,0.001025582,0.0006037802,0.0008400941,0.002102212,0.001360777,0.0009237648],"category_scores_gemma":[0.01898567,0.000466006,0.00156123,0.0006120011,0.0009893747,0.0009840506,0.002194445,0.001825573,0.0004572715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001519814,"about_ca_system_score_gemma":0.002062836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01371003,"about_ca_topic_score_gemma":0.01017838,"domain_scores_codex":[0.9959005,0.002445113,0.000267904,0.0006977,0.0004611043,0.0002277092],"domain_scores_gemma":[0.9882825,0.00610724,0.0009173161,0.001940177,0.00230658,0.0004463282],"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":[0.009002348,0.005540767,0.2947572,0.0007305534,0.005435022,0.0006337019,0.0003918629,0.4678382,0.007538106,0.001128266,0.01804332,0.1889606],"study_design_scores_gemma":[0.0005722687,0.002066754,0.02651825,0.0000985891,0.000469276,0.0002233187,0.00007051129,0.9649598,0.003373701,0.0004744039,0.00112469,0.00004840659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.966974,0.001694506,0.02674459,0.0003782763,0.0001461534,0.0004006264,0.001612092,0.001101479,0.0009481782],"genre_scores_gemma":[0.9692168,0.0003734762,0.01993445,0.000303778,0.0000454387,0.0003669446,0.00851581,0.0001413317,0.001102043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02183428,"threshold_uncertainty_score":0.1154721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02633241694962722,"score_gpt":0.3052077246083451,"score_spread":0.2788753076587178,"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."}}