{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000598365,0.0004924717,0.0002888927,0.0004767794,0.0002081036,0.0006580543,0.000435885,0.0003284405,0.0005550132],"category_scores_gemma":[0.0009180165,0.0002150074,0.0002147966,0.0003096945,0.0004854822,0.0002812934,0.0003780927,0.0003497521,0.0002674229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003191391,"about_ca_system_score_gemma":0.0004169468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004980922,"about_ca_topic_score_gemma":0.001100284,"domain_scores_codex":[0.9991347,0.0001319293,0.00004828532,0.0002127845,0.0004244449,0.00004786225],"domain_scores_gemma":[0.9996201,0.0001363187,0.0001021693,0.00003509571,0.00008265836,0.00002360344],"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":[0.0001034034,0.00001441435,0.0004622901,0.00004618402,0.00001057747,0.00001724497,0.00003135089,0.00008241762,0.9937456,0.00007020656,0.00004085602,0.005375455],"study_design_scores_gemma":[0.00001072544,0.0002416921,0.004411246,0.000005466484,0.00002369271,0.0001612687,0.00001951464,0.002115989,0.9920967,0.00005180706,0.0008487995,0.00001315777],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7853117,0.00267457,0.2061615,0.0002271436,0.00012414,0.0005980916,0.00132351,0.00101849,0.00256086],"genre_scores_gemma":[0.7564062,0.002932429,0.2346529,0.0002818435,0.00008324341,0.0006643988,0.001207783,0.00009033453,0.003680803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006580543,"threshold_uncertainty_score":0.00316453,"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."}}