{"id":"W2015659263","doi":"10.1039/c4lc01251h","title":"On-chip sample preparation for complete blood count from raw blood","year":2015,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital; University of Toronto; Toronto Public Health","funders":"","keywords":"Microfluidics; Chip; Biomedical engineering; Modular design; Materials science; Lab-on-a-chip; Coulter counter; Filtration (mathematics); Sample (material); Sample preparation; Dilution; Whole blood; Chromatography; Blood cell; Microfluidic chip; Analytical Chemistry (journal); Computer science; Chemistry; Nanotechnology; Mathematics; Engineering; Surgery; Immunology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001148335,0.0001813107,0.0002012427,0.0000599859,0.00005938526,0.00004745325,0.0001620562,0.000145607,0.00001904416],"category_scores_gemma":[0.0001870117,0.0001604552,0.00005358629,0.00008702197,0.00003706803,0.00003866177,0.00002799746,0.000136992,0.00007759714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003506849,"about_ca_system_score_gemma":0.00001946223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008541928,"about_ca_topic_score_gemma":0.0000483267,"domain_scores_codex":[0.9991857,0.0000156172,0.0001825695,0.0002296207,0.0001503231,0.0002361815],"domain_scores_gemma":[0.9992785,0.0002060861,0.00003663907,0.0003812986,0.00003935401,0.00005812823],"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":[0.0007771599,0.0008460852,0.001381991,0.0001909097,0.0007415076,0.00003242147,0.002148277,0.003752393,0.6560806,0.04729489,0.2802482,0.006505535],"study_design_scores_gemma":[0.006021834,0.001671063,0.0009887626,0.0002245289,0.0002689557,0.00001597151,0.0002058858,0.0190025,0.7808393,0.05407085,0.1357006,0.0009896859],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888851,0.0006169325,0.006932058,0.0002211396,0.0003757591,0.0003506792,0.0002920259,0.001093677,0.001232607],"genre_scores_gemma":[0.9939628,0.00005600287,0.005271981,0.0002391667,0.000146725,0.00002261885,0.0001977603,0.00003413352,0.00006882942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1445476,"threshold_uncertainty_score":0.6543173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03616815332827959,"score_gpt":0.2404007280621498,"score_spread":0.2042325747338702,"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."}}