{"id":"W2886319298","doi":"10.1155/2018/2583215","title":"Biomarker Profiling for Pyridoxine Dependent Epilepsy in Dried Blood Spots by HILIC-ESI-MS","year":2018,"lang":"en","type":"article","venue":"International Journal of Analytical Chemistry","topic":"Metabolism and Genetic Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Vision Group on Science and Technology; Grand Challenges Canada","keywords":"Dried blood; Pyridoxine; Hydrophilic interaction chromatography; Dried blood spot; Profiling (computer programming); Spots; Chromatography; Chemistry; Epilepsy; Medicine; Biochemistry; Computer science; High-performance liquid chromatography; Psychiatry","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":[0.0008013353,0.0006344132,0.0004102534,0.0008017023,0.0002691631,0.0004243625,0.0004318683,0.0007821285,0.0007503318],"category_scores_gemma":[0.000867137,0.0002131723,0.0002933267,0.0004434682,0.0003602368,0.00034314,0.0002940763,0.000430021,0.0004711007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002234888,"about_ca_system_score_gemma":0.0003228899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000513006,"about_ca_topic_score_gemma":0.0009654366,"domain_scores_codex":[0.9994442,0.0001241674,0.0000418349,0.0001508161,0.0001972299,0.00004185035],"domain_scores_gemma":[0.9997153,0.00008545089,0.00007586879,0.00001565839,0.00008755183,0.00002006844],"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":[0.0001354834,0.00005871144,0.001462313,0.0001668293,0.00003708334,0.0001647445,0.00004511899,0.00020656,0.9891438,0.00007927674,0.0001244731,0.008375688],"study_design_scores_gemma":[0.00002080541,0.0004216653,0.009510588,0.00002081946,0.00007505195,0.001612193,0.00005014115,0.007279504,0.9786335,0.0001089667,0.00222659,0.00004030154],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8214072,0.01008518,0.1619041,0.0004507606,0.0002426247,0.0004330964,0.001275032,0.001553566,0.002648395],"genre_scores_gemma":[0.818328,0.006176138,0.169032,0.0007739055,0.00008261843,0.0003938154,0.001065354,0.00007696142,0.00407118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008017023,"threshold_uncertainty_score":0.00423795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009809663382744927,"score_gpt":0.2888316566647982,"score_spread":0.2790219932820533,"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."}}