{"id":"W4400324405","doi":"10.1080/20565623.2024.2355038","title":"A comprehensive approach for detection of biotin deficiency from dried blood spot samples using liquid chromatography-mass spectrometry","year":2024,"lang":"en","type":"article","venue":"Future Science OA","topic":"Biotin and Related Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Grand Challenges Canada","keywords":"Dried blood spot; Chromatography; Mass spectrometry; Liquid chromatography–mass spectrometry; Chemistry; Dried blood; High-performance liquid chromatography; Population; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001058613,0.0009675656,0.0008830429,0.002506311,0.0006039845,0.0007436844,0.0008564917,0.001107803,0.0009559509],"category_scores_gemma":[0.001277718,0.0003760696,0.000469534,0.0009232954,0.0005240613,0.0005716567,0.0009729544,0.001093221,0.0008727941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004041539,"about_ca_system_score_gemma":0.001002556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001221925,"about_ca_topic_score_gemma":0.003222887,"domain_scores_codex":[0.9981508,0.0003785549,0.0001008239,0.0003682766,0.0009368243,0.00006477719],"domain_scores_gemma":[0.9993917,0.0001299964,0.0001034628,0.00004775013,0.0002676092,0.00005953031],"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":[0.0001922584,0.0001495261,0.005979096,0.0004305495,0.0001134815,0.000287082,0.00006887714,0.0002344302,0.9402915,0.0003188963,0.0004093299,0.0515251],"study_design_scores_gemma":[0.00006363994,0.001145264,0.0356096,0.0001343605,0.0002697046,0.006772644,0.0001638792,0.009999874,0.9312357,0.0008366466,0.01359624,0.0001724339],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2588,0.02883879,0.6974929,0.001106083,0.0004812195,0.001547837,0.002221742,0.003208759,0.006302789],"genre_scores_gemma":[0.3747151,0.01019007,0.6059363,0.001159971,0.000139128,0.0009372116,0.0012493,0.00007873544,0.005594089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002506311,"threshold_uncertainty_score":0.005598545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01409393003293624,"score_gpt":0.2548191082635245,"score_spread":0.2407251782305883,"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."}}