{"id":"W1500554051","doi":"10.1002/bdd.1771","title":"Utility of a physiologically–based pharmacokinetic (PBPK) modeling approach to quantitatively predict a complex drug–drug–disease interaction scenario for rivaroxaban during the drug review process: implications for clinical practice","year":2012,"lang":"en","type":"article","venue":"Biopharmaceutics & Drug Disposition","topic":"Atrial Fibrillation Management and Outcomes","field":"Medicine","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Physiologically based pharmacokinetic modelling; Drug; Rivaroxaban; Pharmacokinetics; Pharmacology; Medicine; Drug-drug interaction; Clinical Practice; Internal medicine; Warfarin","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001835952,0.0003033665,0.0005525913,0.0001257199,0.0003545734,0.00003465163,0.0002223291,0.00004406329,0.00002434265],"category_scores_gemma":[0.0009712846,0.0002294234,0.0004989647,0.0004272692,0.0001012127,0.00042211,0.00007839669,0.0002330382,0.000007453295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008330377,"about_ca_system_score_gemma":0.0001098627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008616558,"about_ca_topic_score_gemma":8.079545e-7,"domain_scores_codex":[0.9972576,0.0003754762,0.00109857,0.0004949229,0.0003567027,0.0004167038],"domain_scores_gemma":[0.9968931,0.0009548041,0.0006232707,0.0003771729,0.0007966837,0.0003549653],"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":[0.1471159,0.02107054,0.3155555,0.1632108,0.006700395,0.000006108511,0.01500458,0.05025543,0.1382163,0.01181277,0.08843129,0.04262037],"study_design_scores_gemma":[0.007636014,0.0001418784,0.186543,0.001803339,0.007336107,0.000007575964,0.001331964,0.7618384,0.002136416,0.0002127305,0.03030635,0.00070619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8298597,0.007651782,0.09658631,0.04422364,0.000664304,0.01960653,0.0007324795,0.0002831414,0.0003921058],"genre_scores_gemma":[0.9846812,0.00129514,0.009150939,0.002722829,0.0008076128,0.0004804663,0.0007377676,0.00004003432,0.00008402341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.711583,"threshold_uncertainty_score":0.9355615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3069520907949277,"score_gpt":0.517580326192789,"score_spread":0.2106282353978613,"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."}}