{"id":"W4296501315","doi":"10.1007/978-1-0716-2565-1_19","title":"Quantitation of IgG Subclasses in Serum Using Liquid Chromatography–Tandem Mass Spectrometry (LC–MS/MS)","year":2022,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Blood groups and transfusion","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; St. Paul's Hospital","funders":"","keywords":"Chromatography; Liquid chromatography–mass spectrometry; Calibration curve; Tandem mass spectrometry; Chemistry; Subclass; Mass spectrometry; Matrix (chemical analysis); Antibody; Detection limit; Immunology; Medicine","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":[0.001603834,0.0001538193,0.0005154482,0.0008546464,0.00004741823,0.000003365228,0.0001240506,0.0001516706,0.0001568485],"category_scores_gemma":[0.0001387457,0.0001531136,0.0001471489,0.001427392,0.00009526385,0.00002431425,0.00007188148,0.0003982599,4.220699e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007373125,"about_ca_system_score_gemma":0.00007651537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002982319,"about_ca_topic_score_gemma":0.00004628296,"domain_scores_codex":[0.9974123,0.00131925,0.0004613381,0.0003602266,0.000147385,0.0002995351],"domain_scores_gemma":[0.9993652,0.0001684127,0.0001194385,0.0002664896,0.00003654327,0.00004398271],"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":[0.0001947748,0.0001895375,0.09544473,0.00006293412,0.00005225682,0.00009791699,0.0001161764,0.00009795871,0.8996986,0.003343397,0.000001004472,0.0007007735],"study_design_scores_gemma":[0.003601206,0.003175357,0.05695148,0.0001042919,0.0001608069,0.0001649456,0.0005874168,0.002241524,0.9239641,0.008235964,0.0004820712,0.0003308868],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6459067,0.001283703,0.3519925,0.0001497535,0.0001562437,0.0002191234,0.00000682285,0.00001485637,0.0002703839],"genre_scores_gemma":[0.5731636,0.00007541307,0.426559,0.000133106,0.00001430847,0.00001611226,0.00001799392,0.00001740566,0.000003050837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07456657,"threshold_uncertainty_score":0.6243793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03110449545698471,"score_gpt":0.3887490908951939,"score_spread":0.3576445954382093,"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."}}