{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002069636,0.0007905874,0.0009171972,0.0005443954,0.0005628719,0.001874363,0.001002948,0.001869561,0.001864837],"category_scores_gemma":[0.007590153,0.0005365589,0.001014473,0.000343383,0.0004592889,0.001173085,0.0005652671,0.00119368,0.0004675181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001515273,"about_ca_system_score_gemma":0.003464028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0111437,"about_ca_topic_score_gemma":0.005869213,"domain_scores_codex":[0.9992637,0.0004421126,0.00004391054,0.00008846688,0.0001188418,0.00004291586],"domain_scores_gemma":[0.9966913,0.002432777,0.0003177089,0.00009773779,0.000352759,0.000107694],"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":[0.00003664561,0.00003787585,0.00162637,0.00003058397,0.00003955227,0.00008603195,0.00004508536,0.9917122,0.0007799076,0.001780996,0.0002400839,0.003584569],"study_design_scores_gemma":[0.00001389401,0.00005369826,0.0002913643,0.000008084518,0.00001293199,0.00004388651,0.00001810125,0.9973015,0.0001937309,0.00159865,0.0004545153,0.000009674101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2914043,0.001128239,0.6858044,0.004515898,0.0001664848,0.0007053583,0.0008983162,0.0006558431,0.01472114],"genre_scores_gemma":[0.9111655,0.0007700689,0.08468276,0.0004391702,0.00006051053,0.0005046047,0.0002941143,0.00007018736,0.002013045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0111437,"threshold_uncertainty_score":0.02215767,"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."}}