{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001740695,0.0004269367,0.0002695958,0.0002666037,0.0002815384,0.0003862046,0.0003512408,0.0002893407,0.0008025989],"category_scores_gemma":[0.0002649674,0.0002212946,0.0002341548,0.00017163,0.000228835,0.0002024544,0.0003841831,0.0004688878,0.0003996104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004696218,"about_ca_system_score_gemma":0.0005544691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007322472,"about_ca_topic_score_gemma":0.001103907,"domain_scores_codex":[0.9997844,0.00001199928,0.00001244296,0.00007981867,0.0000688487,0.00004253023],"domain_scores_gemma":[0.9998907,0.00003812616,0.00002057974,0.00001394939,0.00001819259,0.00001837272],"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":[0.00002894978,0.00002230423,0.0002830683,0.00003967906,0.000005804596,0.00002738989,0.00002980862,0.0004521603,0.9922496,0.0003465714,0.0004514351,0.006063151],"study_design_scores_gemma":[0.00001989936,0.0001295619,0.002237667,0.000007997252,0.00001313543,0.00007168989,0.00001613634,0.008902313,0.983568,0.0001968531,0.004822827,0.00001397792],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8194327,0.002207421,0.1669175,0.0007392032,0.0002842527,0.0002580876,0.00124064,0.001960722,0.006959507],"genre_scores_gemma":[0.8510717,0.001450505,0.1400052,0.0003935589,0.0001122874,0.0005877284,0.001090505,0.0001326372,0.005155931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008025989,"threshold_uncertainty_score":0.003407359,"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."}}