{"id":"W3156890979","doi":"10.3390/mi12040433","title":"A Microflow Cytometry-Based Agglutination Immunoassay for Point-of-Care Quantitative Detection of SARS-CoV-2 IgM and IgG","year":2021,"lang":"en","type":"article","venue":"Micromachines","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Mitacs","keywords":"Immunoassay; Detection limit; Antibody; Point-of-care testing; Agglutination (biology); Flow cytometry; Chromatography; Chemistry; Point of care; Immunoglobulin M; Immunoglobulin G; Immunology; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00121783,0.001046605,0.0007091563,0.0008472687,0.0003499625,0.0005212046,0.000738213,0.0009727242,0.0008188449],"category_scores_gemma":[0.0009914718,0.0003670977,0.000505591,0.0003875738,0.0004085222,0.0004292392,0.0004137928,0.0009442457,0.0005411496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007709357,"about_ca_system_score_gemma":0.0006674938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007420693,"about_ca_topic_score_gemma":0.001138601,"domain_scores_codex":[0.9987393,0.0002131328,0.00008544738,0.0003014766,0.0005704682,0.00009021883],"domain_scores_gemma":[0.9994944,0.0001483801,0.00008959493,0.00004963186,0.0001631811,0.00005474416],"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":[0.00004947971,0.00004108476,0.0003165221,0.00008029773,0.000009403664,0.0000324988,0.00002110422,0.0001117754,0.9910806,0.000208576,0.0002903083,0.007758353],"study_design_scores_gemma":[0.00002675493,0.0003654704,0.002432999,0.00001233365,0.00002172921,0.0004838622,0.00001115912,0.007440928,0.9842238,0.0001254577,0.004819114,0.00003655483],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2592674,0.006481473,0.7188305,0.0007894495,0.0007609813,0.001786187,0.001348048,0.005458059,0.005278002],"genre_scores_gemma":[0.4716311,0.002625507,0.5160215,0.000792455,0.0002887456,0.001712199,0.001247627,0.00007563836,0.005605172],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00121783,"threshold_uncertainty_score":0.00644058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01420074580584579,"score_gpt":0.2676951372940993,"score_spread":0.2534943914882535,"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."}}