{"id":"W2981693078","doi":"10.1148/radiol.2019190450","title":"Chronic Obstructive Pulmonary Disease: Thoracic CT Texture Analysis and Machine Learning to Predict Pulmonary Ventilation","year":2019,"lang":"en","type":"article","venue":"Radiology","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Institute for Cancer Research","keywords":"Medicine; COPD; Support vector machine; Receiver operating characteristic; Confidence interval; Ventilation (architecture); Pulmonary function testing; Ground truth; Test set; Artificial intelligence; Machine learning; Data set; Radiology; Nuclear medicine; Internal medicine; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003092822,0.0005852342,0.0007784587,0.001087875,0.000215136,0.00104828,0.0004218724,0.0007100225,0.001411568],"category_scores_gemma":[0.007449812,0.0001913326,0.0006454397,0.0007027528,0.0003743809,0.000534215,0.0005080799,0.0006063944,0.0002338913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005298824,"about_ca_system_score_gemma":0.0006284671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002379837,"about_ca_topic_score_gemma":0.002871545,"domain_scores_codex":[0.9990003,0.0004572608,0.00009766748,0.000197902,0.00019059,0.00005640841],"domain_scores_gemma":[0.9981769,0.0009283632,0.000456696,0.0001076901,0.0002473399,0.00008297785],"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":[0.003428497,0.001109819,0.4934194,0.0009814746,0.001283057,0.0002931354,0.0001017117,0.03231334,0.01297526,0.0009818025,0.005121212,0.4479913],"study_design_scores_gemma":[0.0002983279,0.002310901,0.484401,0.0004187897,0.0006318951,0.00138224,0.0001725993,0.4934999,0.007893358,0.005036486,0.003855473,0.00009899771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8837819,0.01816788,0.08803226,0.003729923,0.0002837803,0.0004852875,0.001630548,0.000412408,0.003475953],"genre_scores_gemma":[0.9769446,0.001630091,0.01948068,0.0003055329,0.0001749614,0.0001470562,0.0006655962,0.00001822639,0.0006332357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003092822,"threshold_uncertainty_score":0.01635665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008365162872734944,"score_gpt":0.2696607413291806,"score_spread":0.2612955784564457,"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."}}