{"id":"W2058884267","doi":"10.1016/j.jchromb.2009.01.024","title":"High performance liquid chromatography using UV detection for the quantification of milrinone in plasma: Improved sensitivity for inhalation","year":2009,"lang":"en","type":"article","venue":"Journal of Chromatography B","topic":"Heart Failure Treatment and Management","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Heart Institute; Centre Hospitalier Universitaire Sainte-Justine; Université de Montréal","funders":"Centre hospitalier universitaire Sainte-Justine; Université de Montréal; Sanofi","keywords":"Milrinone; Chromatography; Chemistry; Calibration curve; High-performance liquid chromatography; Solid phase extraction; Extraction (chemistry); Quantitative analysis (chemistry); Cartridge; Matrix (chemical analysis); Detection limit; Anesthesia; Hemodynamics; Materials science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002526136,0.0008932733,0.0009023426,0.0006194016,0.0006351232,0.00114996,0.001147189,0.002384945,0.001704205],"category_scores_gemma":[0.003176953,0.0007761528,0.0005136019,0.0003508901,0.000673085,0.001124499,0.0009398234,0.002617361,0.00117375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003978329,"about_ca_system_score_gemma":0.001261772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007249978,"about_ca_topic_score_gemma":0.001572983,"domain_scores_codex":[0.9975442,0.001057444,0.0001138279,0.0004306333,0.0007067075,0.0001470898],"domain_scores_gemma":[0.9982457,0.0009373057,0.0001664759,0.0001947621,0.0002889922,0.0001667435],"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":[0.0008484292,0.0002537704,0.002465207,0.0002063864,0.00007144428,0.00009283037,0.00008000075,0.0002397014,0.9558535,0.000414626,0.0007334081,0.03874075],"study_design_scores_gemma":[0.0001016158,0.0008809801,0.006390556,0.00005469146,0.00009340394,0.001497145,0.00002985828,0.009812608,0.9774024,0.0002784586,0.003390599,0.00006762691],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4828366,0.02380626,0.4782078,0.002937881,0.001161458,0.0006233593,0.0005090586,0.00497281,0.004944602],"genre_scores_gemma":[0.7749383,0.004466903,0.2095936,0.004160849,0.0004274192,0.0004110502,0.0004558479,0.0002667301,0.005279328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002526136,"threshold_uncertainty_score":0.01335961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02063617755231613,"score_gpt":0.2709936923635999,"score_spread":0.2503575148112838,"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."}}