{"id":"W2910218371","doi":"10.14814/phy2.13955","title":"Oscillometry and pulmonary magnetic resonance imaging in asthma and COPD","year":2019,"lang":"en","type":"article","venue":"Physiological Reports","topic":"Chronic Obstructive Pulmonary Disease (COPD) Research","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; McGill University Health Centre; Robarts Clinical Trials; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Reseau canadien de recherche respiratoire","keywords":"COPD; Medicine; Asthma; Internal medicine; Cardiology; Magnetic resonance imaging; Respiratory system; Airway obstruction; Pulmonary disease; Ventilation (architecture); Lung; Airway; Radiology; Anesthesia","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":[0.001148843,0.0003140215,0.000330183,0.001048577,0.0001847817,0.0006830729,0.0003080439,0.0005631963,0.001268885],"category_scores_gemma":[0.003390006,0.0001519291,0.0002874405,0.0009727933,0.0003930574,0.0003771301,0.0006134915,0.0005762579,0.0002655683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001760084,"about_ca_system_score_gemma":0.0002227952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001537787,"about_ca_topic_score_gemma":0.001580826,"domain_scores_codex":[0.9993176,0.0003009639,0.00006654477,0.0001277228,0.0001379578,0.00004914782],"domain_scores_gemma":[0.9985084,0.0005109925,0.0006335436,0.00008635272,0.0001215302,0.0001392105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003766294,0.00007044146,0.9773052,0.00007443692,0.0001920315,0.0001736134,0.0001340943,0.0001463707,0.00179952,0.0002412154,0.0001663575,0.01932016],"study_design_scores_gemma":[0.000005387375,0.00008444006,0.998647,0.00001318439,0.00003352748,0.0002789882,0.0000625876,0.0002778639,0.00009831455,0.0001766214,0.0003185331,0.000003436014],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9727197,0.0201496,0.001575936,0.0005619279,0.00009058048,0.0000264942,0.0002618974,0.00003354372,0.004580482],"genre_scores_gemma":[0.9958767,0.00232924,0.0008128762,0.00009763034,0.0001570812,0.00001389253,0.0001974012,0.000004228748,0.0005109745],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001537787,"threshold_uncertainty_score":0.006075799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01340889257364844,"score_gpt":0.2795363985224745,"score_spread":0.266127505948826,"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."}}