{"id":"W4288457171","doi":"10.1016/j.ejps.2022.106272","title":"A computed tomography imaging-based subject-specific whole-lung deposition model","year":2022,"lang":"en","type":"article","venue":"European Journal of Pharmaceutical Sciences","topic":"Inhalation and Respiratory Drug Delivery","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"National Center for Advancing Translational Sciences; National Center for Research Resources; National Institute of Environmental Health Sciences; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute","keywords":"Particle deposition; Deposition (geology); Lung volumes; Lung; Biomedical engineering; Functional residual capacity; Particle (ecology); Airflow; Ventilation (architecture); Tomography; Airway; Aerosol; Materials science; Radiology; Physics; Medicine; Geology; Meteorology","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":[0.0005595157,0.0009128746,0.000782838,0.0006384185,0.0002761719,0.001171059,0.002089169,0.002033762,0.002073392],"category_scores_gemma":[0.001158078,0.0007706386,0.001377491,0.00074848,0.0006276533,0.0008760932,0.0008836869,0.0009510044,0.0007382336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008215228,"about_ca_system_score_gemma":0.001524002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01060031,"about_ca_topic_score_gemma":0.004844368,"domain_scores_codex":[0.9996883,0.00007362217,0.00002127119,0.0001078278,0.00007689387,0.0000322777],"domain_scores_gemma":[0.9996781,0.0001099291,0.00006235032,0.00003376593,0.00008149086,0.00003432312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005722068,0.0000355743,0.001400031,0.000085422,0.00004567551,0.0003138272,0.00004567899,0.9798092,0.006815071,0.00446222,0.0007217796,0.006208233],"study_design_scores_gemma":[0.00001308584,0.00003451481,0.0005191583,0.000007467557,0.00003678799,0.000156547,0.000006856008,0.9963194,0.0006119196,0.001001586,0.001281632,0.00001090121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05348996,0.001865418,0.9327672,0.0008540438,0.0001638075,0.0002524102,0.001667713,0.0006188125,0.00832078],"genre_scores_gemma":[0.8295854,0.003719752,0.1317355,0.000946462,0.0002329316,0.001246795,0.00264227,0.0003417793,0.02954908],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01060031,"threshold_uncertainty_score":0.02107722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04690801839506306,"score_gpt":0.3205625532497162,"score_spread":0.2736545348546531,"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."}}