{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001438397,0.0001677234,0.0003316996,0.000180261,0.0001234223,0.00002194478,0.0001558908,0.00003761825,0.0004814615],"category_scores_gemma":[0.00000633385,0.0001544308,0.0001357538,0.0004224128,0.00006628117,0.0001212691,0.0001280894,0.0003650829,0.0001063043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001011624,"about_ca_system_score_gemma":0.0001102348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008977143,"about_ca_topic_score_gemma":0.000001814936,"domain_scores_codex":[0.9987645,0.000174614,0.0001705062,0.0004413225,0.0001291374,0.0003198824],"domain_scores_gemma":[0.9993613,0.0001000645,0.00007363054,0.0002486458,0.00003362182,0.0001827163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009160076,0.00002407339,0.8453757,0.00001853614,0.0003242178,0.0000147521,0.00009132229,0.003090456,0.001749446,0.0008837137,0.00001482052,0.1483214],"study_design_scores_gemma":[0.0001490137,0.00003134243,0.6842329,0.0000119857,0.0002204957,0.00001028473,0.000137345,0.3088418,0.0002153317,0.003806341,0.00210445,0.0002386642],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859301,0.000786537,0.01008442,0.0001058901,0.00007494396,0.0003029365,0.00008307152,0.00003119357,0.002600913],"genre_scores_gemma":[0.9974871,0.00001223216,0.00004001975,0.00001436542,0.0003219181,0.00003094619,0.0004758828,0.0000195134,0.001597988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3057514,"threshold_uncertainty_score":0.6297505,"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."}}