{"id":"W4410419516","doi":"10.1093/toxsci/kfaf071","title":"Quantitative and qualitative concordance between clinical and nonclinical toxicity data","year":2025,"lang":"en","type":"article","venue":"Toxicological Sciences","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"Office of Research and Development; U.S. Environmental Protection Agency","keywords":"Concordance; Toxicity; Rodent; In vivo; Toxicology; Pharmacology; Medicine; Biology; Internal medicine; Biotechnology; Ecology","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.009710008,0.0001565831,0.00043521,0.00008170353,0.0003845814,0.0003001299,0.00184154,0.0001180626,0.000008480775],"category_scores_gemma":[0.005785484,0.0001127002,0.00004062261,0.0008216105,0.003367824,0.0008757206,0.002761123,0.000289265,0.000007379738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001593113,"about_ca_system_score_gemma":0.0002609529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000203293,"about_ca_topic_score_gemma":0.00002518969,"domain_scores_codex":[0.9955885,0.001952307,0.0006091322,0.001193008,0.0003570152,0.0002999742],"domain_scores_gemma":[0.9784891,0.02070192,0.0001553495,0.0004130882,0.00007250781,0.0001680268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00002753667,0.0001369802,0.05593747,0.00001156374,0.00003276052,0.000006466223,0.0005754135,0.00006099444,0.00005217942,0.8685102,0.0006168226,0.07403165],"study_design_scores_gemma":[0.000452199,0.001043693,0.5088664,0.00003534971,0.00001529699,0.000003259619,0.0004991557,0.1293729,0.00005405403,0.3580873,0.001328356,0.0002419998],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6664141,0.0003352764,0.3218926,0.009156001,0.0001934674,0.0001759142,0.00001803372,0.00006575334,0.001748899],"genre_scores_gemma":[0.7366509,0.00006320018,0.2615669,0.001630972,0.00003379311,0.000005846611,0.000002184323,0.000001391991,0.00004488232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5104228,"threshold_uncertainty_score":0.9993445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4523981921971537,"score_gpt":0.5866187416863993,"score_spread":0.1342205494892456,"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."}}