{"id":"W4400622862","doi":"10.1016/j.taap.2024.117034","title":"Can we do better with Mylotarg? Model-based assessment of opportunities to improve therapeutic index","year":2024,"lang":"en","type":"article","venue":"Toxicology and Applied Pharmacology","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Fractal Systems (Canada)","funders":"","keywords":"Dosing; Pharmacology; Clinical trial; Toxicity; Pharmacokinetics; Adverse effect; Drug; Therapeutic index; Schedule; Medicine; Prioritization; Food and drug administration; Pharmacodynamics; Dose; Intensive care medicine; Internal medicine; Computer science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003682133,0.0002757515,0.0005088108,0.0002243098,0.0001102907,0.00001537907,0.0001910471,0.0004471774,0.001340869],"category_scores_gemma":[0.000002263186,0.0001992255,0.00006166549,0.0001375866,0.000788127,0.00002393787,0.00009765863,0.0005536223,0.000008391216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004511965,"about_ca_system_score_gemma":0.000318112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003775674,"about_ca_topic_score_gemma":0.000008314053,"domain_scores_codex":[0.9984747,0.0001939363,0.000320877,0.0005018076,0.00004252268,0.0004662092],"domain_scores_gemma":[0.9992719,0.0003692959,0.00007019987,0.0001654232,0.0000419543,0.00008119937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006477398,0.0002443579,0.0004301961,0.0001621017,0.0007584682,0.00002382279,0.000194324,0.0003243664,0.9152862,0.02133186,0.002207986,0.05838857],"study_design_scores_gemma":[0.006137953,0.004007374,0.003057557,0.00009187132,0.001692877,0.0000972473,0.0005235473,0.01358031,0.8682806,0.01139561,0.08994831,0.001186763],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8754766,0.005509929,0.03999764,0.06202736,0.002091333,0.002155754,0.000412325,0.0003177269,0.01201134],"genre_scores_gemma":[0.9843024,0.0001055991,0.002162309,0.01230092,0.00005108578,0.0001948724,0.00001288503,0.00002577005,0.0008441453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1088258,"threshold_uncertainty_score":0.999572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03795955727374511,"score_gpt":0.3382090716434077,"score_spread":0.3002495143696626,"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."}}