{"id":"W2902282663","doi":"10.1016/j.jchromb.2018.12.003","title":"An LC-MS/MS method for quantification of abiraterone, its active metabolites D(4)-abiraterone (D4A) and 5α-abiraterone, and their inactive glucuronide derivatives","year":2018,"lang":"en","type":"article","venue":"Journal of Chromatography B","topic":"Prostate Cancer Treatment and Research","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Centre hospitalier de l'Université Laval; Centre hospitalier universitaire de Québec","funders":"Fondation CHU de Québec","keywords":"Chemistry; Chromatography; Selected reaction monitoring; Prostate cancer; Abiraterone acetate; Abiraterone; Analyte; Prodrug; CYP17A1; Pharmacology; Tandem mass spectrometry; Mass spectrometry; Androgen deprivation therapy; Androgen receptor; Biochemistry; Cancer; Enzyme; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0007207987,0.0003100664,0.0008381872,0.0006354644,0.0001766766,0.00007228199,0.000147425,0.0001367464,0.00002817623],"category_scores_gemma":[0.00008371969,0.0002022472,0.0001750626,0.0004525642,0.0005528125,0.0007639842,0.00003818715,0.0002189794,7.824428e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005042395,"about_ca_system_score_gemma":0.0001678923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003515308,"about_ca_topic_score_gemma":0.00001466223,"domain_scores_codex":[0.9981071,0.0002525331,0.0006311687,0.0003477121,0.0003058023,0.0003557281],"domain_scores_gemma":[0.9973856,0.0002427938,0.0007087555,0.0002499612,0.001133566,0.0002793463],"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":[0.004006998,0.0004514393,0.01059772,0.0003232353,0.001133032,0.000006966258,0.003768697,9.277675e-7,0.9661964,0.0003832667,0.00009778677,0.01303358],"study_design_scores_gemma":[0.003755364,0.004765558,0.1783893,0.0002512732,0.0002632188,0.0001141827,0.001714073,0.0002467653,0.8087699,0.0009190241,0.0006455613,0.0001658619],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829077,0.00593777,0.009539145,0.0004958668,0.0001252281,0.0007516038,0.0001008634,0.00001928059,0.0001226085],"genre_scores_gemma":[0.9856526,0.001710011,0.01219815,0.00006281999,0.0002420932,0.0000374585,0.00002131607,0.00004285566,0.00003272514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1677915,"threshold_uncertainty_score":0.8247402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03459903610339165,"score_gpt":0.3580245495373112,"score_spread":0.3234255134339195,"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."}}