{"id":"W2945839580","doi":"10.1002/psp4.12414","title":"Physiologically‐Based Pharmacokinetic Modeling of Fluconazole Using Plasma and Cerebrospinal Fluid Samples From Preterm and Term Infants","year":2019,"lang":"en","type":"article","venue":"CPT Pharmacometrics & Systems Pharmacology","topic":"Antifungal resistance and susceptibility","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Center for Advancing Translational Sciences; National Institute of Allergy and Infectious Diseases; National Institute of Child Health and Human Development; National Institute of General Medical Sciences; National Institutes of Health","keywords":"Pharmacokinetics; Cerebrospinal fluid; Dosing; Fluconazole; Medicine; Physiologically based pharmacokinetic modelling; Plasma concentration; Pharmacology; Internal medicine; Antifungal","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":[0.0003662631,0.0004058122,0.0004499708,0.0001574381,0.0002192476,0.0005219852,0.0004107726,0.0005674071,0.0003588798],"category_scores_gemma":[0.001499304,0.0001962857,0.0004565454,0.0001978664,0.0002001827,0.0002308868,0.0002243421,0.0005065891,0.00008052513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006814604,"about_ca_system_score_gemma":0.001173272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01917314,"about_ca_topic_score_gemma":0.006642079,"domain_scores_codex":[0.9998375,0.00006737834,0.00001044992,0.00002891298,0.00003906237,0.00001662749],"domain_scores_gemma":[0.9995953,0.0002616654,0.00005165594,0.00001308267,0.00006675703,0.00001161859],"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":[0.0001575124,0.00005646101,0.006385189,0.00004876247,0.00003445827,0.0001936426,0.00006358127,0.9792819,0.008304108,0.0005173463,0.0001563134,0.00480071],"study_design_scores_gemma":[0.00001936938,0.0001674139,0.003283025,0.000004141645,0.00001584632,0.00006814533,0.00002721339,0.9931773,0.002703367,0.0002832481,0.0002404694,0.00001039322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9589586,0.0002877966,0.0383067,0.0001642412,0.000009610764,0.00007671974,0.0004826309,0.00007761588,0.001636177],"genre_scores_gemma":[0.9930581,0.0001829702,0.00586957,0.000028627,0.000002695437,0.00008000849,0.0002944158,0.000007412235,0.00047619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01917314,"threshold_uncertainty_score":0.03812307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05298117718930536,"score_gpt":0.3324642961910152,"score_spread":0.2794831190017099,"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."}}