{"id":"W3162504224","doi":"10.1016/j.chroma.2021.462228","title":"A quantitative analysis of total and free 11-oxygenated androgens and its application to human serum and plasma specimens using liquid-chromatography tandem mass spectrometry","year":2021,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Hormonal and reproductive studies","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre hospitalier de l'Université Laval; Centre hospitalier universitaire de Québec","funders":"Canadian Institutes of Health Research","keywords":"Chemistry; Chromatography; Derivatization; Liquid chromatography–mass spectrometry; Androsterone; Glucuronidation; Electrospray ionization; Mass spectrometry; Bioanalysis; Ion suppression in liquid chromatography–mass spectrometry; Glucuronide; Tandem mass spectrometry; Quantitative analysis (chemistry); Sample preparation; Epitestosterone; Metabolite; Androgen; Steroid; Enzyme; Biochemistry; Microsome; Hormone","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009596774,0.0009215184,0.0004498008,0.001706839,0.0009079091,0.000612441,0.0005017529,0.0007809722,0.001065045],"category_scores_gemma":[0.001153359,0.0003601354,0.000492939,0.0008809201,0.0009101502,0.0003071152,0.0005403123,0.0009219855,0.0005823477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003376946,"about_ca_system_score_gemma":0.001275934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001786365,"about_ca_topic_score_gemma":0.003435494,"domain_scores_codex":[0.9987633,0.0002332977,0.00007460712,0.0003130171,0.0005355324,0.00008018065],"domain_scores_gemma":[0.9995364,0.0001146835,0.00006053561,0.00004929769,0.0001618745,0.00007716227],"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":[0.0009703694,0.0002598555,0.0162877,0.0002152885,0.0002084864,0.0003080794,0.000252959,0.0002926132,0.948486,0.0006046589,0.0005706382,0.03154342],"study_design_scores_gemma":[0.0001005761,0.001814707,0.09149939,0.00006379241,0.00026952,0.003968098,0.0003060196,0.004287086,0.8827053,0.0009295228,0.01395066,0.0001053479],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8050632,0.02119909,0.1531149,0.0009163956,0.0009132626,0.001036773,0.005474885,0.001611166,0.01067035],"genre_scores_gemma":[0.8998196,0.005861687,0.07865644,0.001609297,0.000339431,0.001191869,0.002575787,0.0001154466,0.009830508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001786365,"threshold_uncertainty_score":0.005075336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01969944372930157,"score_gpt":0.2919423247174645,"score_spread":0.272242880988163,"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."}}