{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002617383,0.0001848212,0.0003909567,0.0001234979,0.00004342689,0.00001889457,0.0000527141,0.00007607311,0.0004736173],"category_scores_gemma":[0.0003074346,0.0001434075,0.00005310769,0.00027704,0.0002964906,0.0001100783,0.0002695357,0.0003106302,0.0000218383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001006848,"about_ca_system_score_gemma":0.00007501302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006168073,"about_ca_topic_score_gemma":0.000001162829,"domain_scores_codex":[0.9981033,0.0000690167,0.0003238282,0.0007471758,0.0003333538,0.000423345],"domain_scores_gemma":[0.9991262,0.0001111623,0.00007267014,0.0003821555,0.00005893151,0.0002488912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001769305,0.0001735772,0.8707771,0.000193523,0.000002971865,0.004681811,0.00001662849,0.000001415962,0.04669202,0.00007371118,0.0001256991,0.07708462],"study_design_scores_gemma":[0.0004439805,0.0001330915,0.9917243,0.000132139,0.000006729346,0.001112716,0.00007779706,0.001032585,0.00008857934,0.002358331,0.002733922,0.0001558271],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9698721,0.02414829,9.242921e-7,0.0003160949,0.00007370308,0.0005417083,0.000004553481,0.0000398013,0.005002853],"genre_scores_gemma":[0.9980519,0.0004716778,0.0001335571,0.0002472229,0.00006772327,0.0000310943,0.00001650063,0.00001735306,0.0009629335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1209472,"threshold_uncertainty_score":0.5847989,"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."}}