{"id":"W2410405443","doi":"10.1021/acs.analchem.6b01190","title":"Hematocrit-Independent Quantitation of Stimulants in Dried Blood Spots: Pipet versus Microfluidic-Based Volumetric Sampling Coupled with Automated Flow-Through Desorption and Online Solid Phase Extraction-LC-MS/MS Bioanalysis","year":2016,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"World Anti-Doping Agency; Partnership for Clean Competition","keywords":"Bioanalysis; Chromatography; Chemistry; Dried blood spot; Dried blood; Pipette; Whole blood; Blood sampling; Sample preparation; Solid phase extraction; Hematocrit; Microfluidics; Sampling (signal processing); Extraction (chemistry); Biomedical engineering; Analytical Chemistry (journal); Nanotechnology; Computer science; Surgery; Materials science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004146109,0.0003088777,0.0007053618,0.0001993423,0.00008416634,0.00002361204,0.0001653089,0.0004747501,0.0006297736],"category_scores_gemma":[0.0006947695,0.0002231117,0.0001476223,0.0008282308,0.0004415226,0.0001261899,0.00004434487,0.0002969259,0.000009730219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009237636,"about_ca_system_score_gemma":0.0001149396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001493313,"about_ca_topic_score_gemma":0.00003793402,"domain_scores_codex":[0.997938,0.0001017093,0.0007627373,0.0005979402,0.0001534274,0.0004461615],"domain_scores_gemma":[0.9981734,0.0009121367,0.000260429,0.0003369646,0.0002313455,0.00008569097],"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":[0.002342163,0.001620785,0.004310951,0.0001499068,0.0007207082,0.00003089103,0.00001789663,0.00005322896,0.9890749,0.00002001874,0.00007331742,0.001585217],"study_design_scores_gemma":[0.01962947,0.0005846913,0.003728639,0.0003912549,0.001761464,0.00005702312,0.0001698167,0.05782691,0.9150137,0.00006491171,0.0002274847,0.0005446493],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9680272,0.001356439,0.02971438,0.0003365139,0.00006444725,0.0001653067,0.0001942799,0.00009654366,0.00004485618],"genre_scores_gemma":[0.9928067,0.0003253544,0.006380777,0.0000483014,0.00002868858,0.000008816518,0.0002416346,0.00002298497,0.0001367463],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07406123,"threshold_uncertainty_score":0.9098232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0434164969134294,"score_gpt":0.3723342084478274,"score_spread":0.328917711534398,"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."}}