{"id":"W3113088690","doi":"10.1016/j.bios.2020.112905","title":"The potential application of electrochemical biosensors in the COVID-19 pandemic: A perspective on the rapid diagnostics of SARS-CoV-2","year":2020,"lang":"en","type":"review","venue":"Biosensors and Bioelectronics","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":152,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Sunnybrook Health Science Centre","funders":"McGill University","keywords":"Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Biosensor; Pandemic; Software portability; 2019-20 coronavirus outbreak; Nanotechnology; Molecular beacon; Point-of-care testing; Molecular diagnostics; Perspective (graphical); Computer science; Virology; Computational biology; Materials science; Medicine; Chemistry; Biology; Bioinformatics; Infectious disease (medical specialty); Disease; Artificial intelligence; Immunology; Outbreak","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":[0.001307927,0.001266523,0.001492439,0.00208598,0.0002281016,0.001491058,0.001181185,0.002553395,0.003264198],"category_scores_gemma":[0.00126576,0.000362329,0.0006486684,0.002150268,0.0007241688,0.002276285,0.0008281904,0.002993246,0.002307035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008934605,"about_ca_system_score_gemma":0.001215192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001495868,"about_ca_topic_score_gemma":0.00297834,"domain_scores_codex":[0.9997026,0.00005429465,0.00002464456,0.00005304956,0.0001284492,0.00003684208],"domain_scores_gemma":[0.999297,0.0003744275,0.00006459172,0.00001600428,0.0001974589,0.00005040042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009683506,0.00009663366,0.0001492403,0.009515837,0.00008340047,0.0001975704,0.00005085796,0.0004715433,0.004472437,0.00960627,0.04517542,0.930084],"study_design_scores_gemma":[0.00002052735,0.0001457315,0.0003827079,0.001729577,0.00006881572,0.0005737517,0.00004598037,0.0001844372,0.0007895657,0.00282548,0.9932086,0.00002488286],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00006694071,0.9980914,0.0002112038,0.0004797787,0.0004366769,0.000002860341,0.00001356801,0.000005094671,0.0006924486],"genre_scores_gemma":[0.000545478,0.9975891,0.0003018292,0.0004787771,0.0003721251,0.000004476955,0.00002269006,0.000001437849,0.0006840497],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003264198,"threshold_uncertainty_score":0.01091981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03796619462239197,"score_gpt":0.3552952624200271,"score_spread":0.3173290677976351,"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."}}