{"id":"W2564611879","doi":"10.1515/rrlm-2016-0039","title":"Comparison of four chromatographic methods used for measurement of glycated hemoglobin","year":2016,"lang":"en","type":"article","venue":"Revista română de medicină de laborator","topic":"Diabetes Management and Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Pipette; Glycated hemoglobin; Chromatography; Spectrum analyzer; Glycated haemoglobin; Hemoglobin; Glycemic; Reagent; Mathematics; Computer science; Chemistry; Analytical Chemistry (journal); Statistics; Biomedical engineering; Medicine; Internal medicine; Diabetes mellitus","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":[0.004046569,0.0007281708,0.0006328772,0.00343373,0.0003908014,0.001096859,0.0008763269,0.0007961084,0.0009224499],"category_scores_gemma":[0.008863108,0.0003363958,0.0007863332,0.002206584,0.0004389173,0.00043184,0.0005521559,0.0004294839,0.000400864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005708171,"about_ca_system_score_gemma":0.0004706631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001591997,"about_ca_topic_score_gemma":0.001737807,"domain_scores_codex":[0.9907699,0.003289635,0.0007440112,0.001045544,0.00388978,0.0002611924],"domain_scores_gemma":[0.9934012,0.002622391,0.001320119,0.0005612312,0.001903639,0.0001913445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00877018,0.001299167,0.5992365,0.001877737,0.002596579,0.000585906,0.0009191928,0.002027427,0.139517,0.0007588582,0.001318698,0.2410927],"study_design_scores_gemma":[0.0002251471,0.008955039,0.7380655,0.0003835123,0.002519515,0.004933419,0.001028564,0.008336561,0.2249767,0.0006362761,0.009784532,0.0001551263],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9618247,0.01697063,0.01580349,0.0002044105,0.0002507977,0.0002128007,0.0008439372,0.000202217,0.003687033],"genre_scores_gemma":[0.9611076,0.004303576,0.03196726,0.0001475399,0.00009875807,0.0001374472,0.000909442,0.00005150269,0.001276887],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004046569,"threshold_uncertainty_score":0.02140057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08246439542053226,"score_gpt":0.4259614871078479,"score_spread":0.3434970916873157,"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."}}