{"id":"W4413984047","doi":"10.1016/j.jcoa.2025.100250","title":"Liquid chromatography coupled to mass spectrometry for steroid hormones analysis: issues and solutions in sample preparation and method development","year":2025,"lang":"en","type":"article","venue":"Journal of Chromatography Open","topic":"Hormonal and reproductive studies","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"CanAm Bioresearch (Canada)","funders":"Russian Science Foundation; National Natural Science Foundation of China","keywords":"Chromatography; Steroid; Mass spectrometry; Sample (material); Chemistry; Hormone; Sample preparation; Liquid chromatography–mass spectrometry; Biochemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.001247818,0.0001784566,0.0008584636,0.002046795,0.0001937003,0.0000886515,0.0001414798,0.00005827864,0.000006155963],"category_scores_gemma":[0.0001517912,0.0001343081,0.0001926562,0.002092638,0.00007818575,0.0002198313,0.0001358748,0.0001152691,2.474012e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003698787,"about_ca_system_score_gemma":0.0001153705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001031919,"about_ca_topic_score_gemma":0.0001780082,"domain_scores_codex":[0.998518,0.00006966703,0.0006563062,0.0003055763,0.0002203992,0.0002300851],"domain_scores_gemma":[0.9990277,0.0001657844,0.0002155371,0.000156886,0.0003083252,0.0001257909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01354825,0.001754037,0.6044374,0.00126038,0.03144607,0.00005473729,0.01142794,0.0001713768,0.3203899,0.002983175,0.005034996,0.007491784],"study_design_scores_gemma":[0.002872025,0.001759117,0.970135,0.0003866438,0.001630445,0.00004037015,0.002442961,0.0002158687,0.01286482,0.001898633,0.005496519,0.0002575758],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9236771,0.01153209,0.06044377,0.002959031,0.0000674709,0.001078488,0.00001096957,0.00001128223,0.0002197626],"genre_scores_gemma":[0.7831964,0.0008292674,0.215682,0.0001105066,0.00005482283,0.00007029533,0.000006433716,0.000008599465,0.00004166919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3656977,"threshold_uncertainty_score":0.5476924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02660254957572826,"score_gpt":0.364648107624467,"score_spread":0.3380455580487387,"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."}}