{"id":"W4399765528","doi":"10.32920/26052826.v1","title":"Quantitative Computed Tomography Imaging and Machine Learning for Evaluating Chronic Obstructive Pulmonary Disease","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pulmonary disease; Computed tomography; Medicine; Quantitative computed tomography; Tomography; Radiology; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001988566,0.0009130783,0.0005642188,0.002785001,0.0002632727,0.001691637,0.0005922139,0.0009786371,0.002214047],"category_scores_gemma":[0.005158321,0.0002615212,0.0005751498,0.002442256,0.000711026,0.0008395368,0.0006462365,0.001351376,0.0009269472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009485409,"about_ca_system_score_gemma":0.001070022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002711717,"about_ca_topic_score_gemma":0.003179153,"domain_scores_codex":[0.9990162,0.0003043146,0.00007750026,0.0001635154,0.000379919,0.00005856636],"domain_scores_gemma":[0.9984955,0.0006897618,0.0002556416,0.0001137426,0.0003715472,0.00007389145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003974318,0.0003520876,0.05829536,0.001425868,0.0002964635,0.0003233397,0.0001774537,0.02664492,0.01735358,0.03258889,0.02922902,0.8329156],"study_design_scores_gemma":[0.0001829049,0.001552028,0.1741163,0.002844396,0.0006317525,0.004234709,0.0009437435,0.5101694,0.02810664,0.09100302,0.1857383,0.0004766808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1138236,0.1903952,0.6307325,0.01072648,0.003090495,0.001548387,0.006112956,0.002317274,0.04125312],"genre_scores_gemma":[0.500761,0.05419147,0.4258571,0.001840564,0.00180764,0.001183311,0.002143057,0.0001653647,0.01205057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002785001,"threshold_uncertainty_score":0.01051664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02679421758697185,"score_gpt":0.3480444189983911,"score_spread":0.3212502014114192,"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."}}