{"id":"W3000588177","doi":"10.1039/c9lc01122f","title":"Microfluidic concentration and separation of circulating tumor cell clusters from large blood volumes","year":2020,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Microsemi (Canada)","funders":"National Cancer Institute; American Cancer Society; National Institute of Biomedical Imaging and Bioengineering; Howard Hughes Medical Institute","keywords":"Microfluidics; Separation (statistics); Circulating tumor cell; Chromatography; Cell; Chemistry; Computational biology; Nanotechnology; Materials science; Biology; Medicine; Internal medicine; Computer science; Cancer; Biochemistry","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":[0.00003920064,0.0001030656,0.0001389012,0.00001511583,0.00003414101,0.00002009659,0.00005612252,0.00006655159,0.00001255837],"category_scores_gemma":[0.00002009282,0.000101777,0.00002443766,0.00008813991,0.00002850125,0.00004628709,0.0000233913,0.00009799887,0.000008671701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001021002,"about_ca_system_score_gemma":0.00000744215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001482674,"about_ca_topic_score_gemma":0.000001313784,"domain_scores_codex":[0.9994729,0.00001540887,0.0001678359,0.0001446283,0.00007110251,0.0001281442],"domain_scores_gemma":[0.9997876,0.00002748805,0.00004196745,0.00009712634,0.0000135888,0.00003219211],"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":[0.000008795582,0.00001452015,0.003050003,0.00006281631,0.00001825505,0.000005133018,0.0006984732,0.00001943756,0.9928792,0.00008209526,0.002655708,0.0005056232],"study_design_scores_gemma":[0.0006464316,0.00006854096,0.001705777,0.00004859604,0.00003761545,0.000002210126,0.0002717061,0.01733281,0.978468,0.00006061293,0.001220125,0.000137594],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932104,0.004205846,0.001559873,0.0001723487,0.00006563528,0.0001065023,0.00004152969,0.0002891598,0.0003486994],"genre_scores_gemma":[0.9987866,0.0004321656,0.0004641808,0.0002204744,0.00004382398,0.00000124343,0.00003169725,0.0000138256,0.000005980946],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01731337,"threshold_uncertainty_score":0.4150346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01037159742457452,"score_gpt":0.2009186795778353,"score_spread":0.1905470821532607,"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."}}