{"id":"W2070320306","doi":"10.1016/j.yrtph.2008.01.011","title":"Development of good modelling practice for physiologically based pharmacokinetic models for use in risk assessment: The first steps","year":2008,"lang":"en","type":"review","venue":"Regulatory Toxicology and Pharmacology","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":126,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Physiologically based pharmacokinetic modelling; Documentation; Risk assessment; Risk analysis (engineering); Computer science; Management science; Data science; Medicine; Engineering; Pharmacokinetics; Pharmacology","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"],"consensus_categories":[],"category_scores_codex":[0.005163474,0.0006261396,0.003259705,0.0002000922,0.0003663403,0.00001580864,0.0005640049,0.001094039,0.00006214053],"category_scores_gemma":[0.00549626,0.0004382819,0.000520414,0.0002343154,0.000614271,0.000120168,0.0001827845,0.001014089,0.000001004361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001826333,"about_ca_system_score_gemma":0.0008747853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000115672,"about_ca_topic_score_gemma":0.000004440754,"domain_scores_codex":[0.9922219,0.003467596,0.002554433,0.0009005324,0.0002167419,0.0006387882],"domain_scores_gemma":[0.82593,0.1715211,0.001860296,0.0003300841,0.0002428418,0.00011569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002851072,0.004878073,0.000007141499,0.04086257,0.004025155,0.00003655515,0.0003822448,0.0167545,0.0000757596,0.06528864,0.004735567,0.8601027],"study_design_scores_gemma":[0.005033503,0.000651638,0.000006587784,0.001063952,0.004467895,0.00002634721,0.0000174687,0.1351955,0.00007553041,0.08215518,0.7706482,0.0006581537],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004796069,0.3670332,0.6207436,0.0001408367,0.001129127,0.009747147,0.0005931198,0.0000605758,0.00007283048],"genre_scores_gemma":[0.00007712372,0.4696967,0.5273309,0.0003125901,0.0001598328,0.002337916,0.00001296285,0.00005582578,0.00001613044],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8594446,"threshold_uncertainty_score":0.9998069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6373778513633809,"score_gpt":0.580341614261531,"score_spread":0.05703623710184991,"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."}}