{"id":"W3107147053","doi":"10.4103/1735-5362.301335","title":"Bayesian estimation of pharmacokinetic parameters: an important component to include in the teaching of clinical pharmacokinetics and therapeutic drug monitoring","year":2020,"lang":"en","type":"review","venue":"Research in Pharmaceutical Sciences","topic":"Antibiotics Pharmacokinetics and Efficacy","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Bayesian probability; Bayes' theorem; Pharmacokinetics; Computer science; A priori and a posteriori; Bayes estimator; Therapeutic drug monitoring; Estimation; Drug; Machine learning; Medicine; Artificial intelligence; Pharmacology; Engineering","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01632073,0.0003989076,0.001852492,0.0008831103,0.0001247,0.0000814634,0.001104877,0.0001596453,0.00001546681],"category_scores_gemma":[0.0007296047,0.0002596658,0.0002432371,0.002151493,0.001317353,0.00009505581,0.0004719759,0.002790958,0.000004011564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000093561,"about_ca_system_score_gemma":0.0005178164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001115965,"about_ca_topic_score_gemma":0.000001928095,"domain_scores_codex":[0.9908206,0.003145455,0.00238208,0.0008710954,0.001950877,0.000829926],"domain_scores_gemma":[0.9948904,0.00361833,0.000377377,0.000350118,0.0001111472,0.0006526644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001500311,0.001028283,0.004386964,0.005866982,0.0000776653,0.000130332,0.0008321593,0.0003099862,0.0002257995,0.0002040174,0.0000312601,0.9867565],"study_design_scores_gemma":[0.007065895,0.004563593,0.00751388,0.03948808,0.002506251,0.0002850638,0.001990895,0.6182652,0.002062231,0.0008350823,0.313852,0.001571915],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.09919743,0.8938963,0.0001172066,0.002321763,0.0003212974,0.003704516,0.00002756559,0.00002214942,0.0003917938],"genre_scores_gemma":[0.4642106,0.5337003,0.001804085,0.00009200867,0.0001408986,0.00002356984,0.00000496013,0.00002164977,0.000001994233],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9851846,"threshold_uncertainty_score":0.9999856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4560855237074387,"score_gpt":0.6181548302208935,"score_spread":0.1620693065134547,"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."}}