{"id":"W3085002087","doi":"10.1002/cyto.b.21949","title":"High‐sensitivity flow cytometric assays: Considerations for design control and analytical validation for identification of Rare events","year":2020,"lang":"en","type":"article","venue":"Cytometry Part B Clinical Cytometry","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Caprion (Canada)","funders":"","keywords":"Sensitivity (control systems); Computer science; Identification (biology); Limit (mathematics); Data mining; Flow (mathematics); Event (particle physics); Rare events; Reliability engineering; Engineering; Statistics; Mathematics; Physics; Biology","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002572491,0.0002747118,0.0007066504,0.0003141429,0.0001797146,0.00006237622,0.0001499547,0.0004625738,0.00001542471],"category_scores_gemma":[0.01587213,0.0002811014,0.0003898056,0.0008628097,0.000202734,0.00002577968,0.00005433987,0.0001883905,0.000005812336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002245937,"about_ca_system_score_gemma":0.0001404619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002638937,"about_ca_topic_score_gemma":0.000001214995,"domain_scores_codex":[0.9968289,0.0004299449,0.001317833,0.0007995035,0.0002830266,0.0003408617],"domain_scores_gemma":[0.9953344,0.002924367,0.000438487,0.0003721991,0.0006151002,0.0003154514],"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":[0.00523246,0.001686635,0.187457,0.0007740038,0.001549091,0.000008477178,0.00007193227,0.002995674,0.7773703,0.000580982,0.01190028,0.01037316],"study_design_scores_gemma":[0.02452084,0.007547621,0.09891558,0.0000982436,0.001807577,0.00003601396,0.0001325273,0.1777732,0.6814143,0.002024341,0.003982723,0.001747075],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3656127,0.00016942,0.6315057,0.0005801851,0.000352319,0.0009783074,0.0007691085,0.0000262608,0.000006045748],"genre_scores_gemma":[0.9775048,0.0001138104,0.02026391,0.0007306273,0.000758022,0.0000916743,0.0004529166,0.00004682056,0.00003744206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6118921,"threshold_uncertainty_score":0.9999641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1144724903759579,"score_gpt":0.3456248632235747,"score_spread":0.2311523728476167,"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."}}