{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.254031,0.002626268,0.003613511,0.003307461,0.001475852,0.008300091,0.00693578,0.007634883,0.003257047],"category_scores_gemma":[0.1765155,0.002041477,0.001992497,0.002303784,0.007506053,0.004777915,0.003453998,0.007790194,0.004729317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003676688,"about_ca_system_score_gemma":0.008311631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002377842,"about_ca_topic_score_gemma":0.002946196,"domain_scores_codex":[0.8179262,0.1096613,0.009355933,0.007717291,0.05344816,0.001891122],"domain_scores_gemma":[0.7414724,0.1754349,0.01072047,0.02272759,0.04802759,0.001617148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002170879,0.0008417521,0.006987867,0.009078665,0.0005830079,0.0007075968,0.001878034,0.01943826,0.2196683,0.1397034,0.05396127,0.544981],"study_design_scores_gemma":[0.0006092271,0.003848805,0.01095159,0.005994529,0.0007166233,0.0031319,0.0008985873,0.06329824,0.3054847,0.07830413,0.5259508,0.0008108852],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005296597,0.01749132,0.9537232,0.00981227,0.001420563,0.003026519,0.0005219404,0.001105429,0.007602054],"genre_scores_gemma":[0.05787322,0.01188044,0.9088123,0.007639629,0.001352363,0.006063331,0.0009730996,0.0006346541,0.004770924],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.254031,"threshold_uncertainty_score":0.9199126,"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."}}