{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06069089,0.000609298,0.0008403463,0.002176324,0.0003541496,0.00244503,0.001212319,0.0007928639,0.001982968],"category_scores_gemma":[0.0846181,0.0004153194,0.0007685008,0.001443395,0.001311446,0.0009227998,0.002081016,0.0009693713,0.0007975305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009587279,"about_ca_system_score_gemma":0.001057113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00105762,"about_ca_topic_score_gemma":0.001420528,"domain_scores_codex":[0.9322268,0.02444411,0.007962352,0.006918265,0.02770189,0.0007466156],"domain_scores_gemma":[0.881662,0.05089651,0.02021504,0.01649115,0.02994143,0.0007938385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003256657,0.0004192847,0.5432716,0.003994472,0.002178642,0.0009699212,0.003929886,0.01151911,0.1729886,0.00560466,0.003812014,0.2480552],"study_design_scores_gemma":[0.0001248048,0.004938038,0.7155217,0.0009299639,0.00166781,0.004288665,0.001905793,0.02113668,0.2069622,0.01029849,0.03197415,0.0002518184],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5413952,0.0114152,0.4010476,0.001130345,0.000483833,0.001981965,0.005289036,0.001396888,0.03585998],"genre_scores_gemma":[0.9464439,0.00118471,0.04690536,0.0006193782,0.0001231106,0.0004140104,0.002343533,0.0001611136,0.001804953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06069089,"threshold_uncertainty_score":0.320968,"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."}}