{"id":"W4200132041","doi":"10.1016/j.clinbiochem.2021.12.004","title":"A highly accurate mass spectrometry method for the quantification of phenylalanine and tyrosine on dried blood spots: Combination of liquid chromatography, phenylalanine/tyrosine-free blood calibrators and multi-point/dynamic calibration","year":2021,"lang":"en","type":"article","venue":"Clinical Biochemistry","topic":"Metabolism and Genetic Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Hospital for Sick Children","funders":"","keywords":"Phenylalanine; Chromatography; Tyrosine; Calibration; Phenylketonurias; Chemistry; Dried blood spot; Detection limit; Mass spectrometry; Tandem mass spectrometry; Phenylalanine hydroxylase; Analytical Chemistry (journal); Biochemistry; Amino acid; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003803977,0.00197747,0.002173861,0.003746296,0.001197293,0.0009462495,0.002727424,0.002814463,0.001490731],"category_scores_gemma":[0.003606541,0.001527224,0.0008711349,0.001827881,0.001246609,0.001397369,0.00172831,0.002646568,0.001740693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000895963,"about_ca_system_score_gemma":0.001538577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001413415,"about_ca_topic_score_gemma":0.003251173,"domain_scores_codex":[0.9937176,0.001109518,0.0002473328,0.001371497,0.003375377,0.0001786671],"domain_scores_gemma":[0.9976794,0.0008231093,0.000241141,0.0002142244,0.0008261749,0.0002159081],"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":[0.0004846461,0.0002663016,0.002523801,0.0002947437,0.0002043641,0.000132366,0.0000839134,0.0005291537,0.919414,0.000672568,0.001618417,0.07377565],"study_design_scores_gemma":[0.0002002211,0.001134254,0.01325763,0.00006155945,0.0002854726,0.005393203,0.00006817443,0.03360093,0.934217,0.001234099,0.01025746,0.0002900699],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07225009,0.007709062,0.9085562,0.0005367607,0.0007475827,0.0008205936,0.001006454,0.006499167,0.001874262],"genre_scores_gemma":[0.1680066,0.003500762,0.8170574,0.001304358,0.0002652476,0.001090541,0.001112158,0.0003300919,0.007332829],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003803977,"threshold_uncertainty_score":0.02011764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01649206972428973,"score_gpt":0.3089424494166862,"score_spread":0.2924503796923965,"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."}}