{"id":"W3120749237","doi":"10.1016/j.ebiom.2020.103183","title":"A breath of fresh air – the potential for COVID-19 breath diagnostics","year":2021,"lang":"en","type":"article","venue":"EBioMedicine","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institute of Environmental Health Sciences; National Institutes of Health; Center for Information Technology Research in the Interest of Society; Canada Economic Development for Quebec Regions; Tobacco-Related Disease Research Program","keywords":"False positive paradox; Pandemic; Gold standard (test); Medicine; Coronavirus disease 2019 (COVID-19); Public health; Diagnostic test; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Test (biology); Computer science; Virology; Risk analysis (engineering); Disease; Pathology; Biology; Infectious disease (medical specialty); Artificial intelligence; Veterinary medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008525371,0.0009056035,0.001291956,0.001734053,0.00124609,0.005727701,0.002396031,0.006761538,0.02006713],"category_scores_gemma":[0.01503196,0.0007733577,0.001472273,0.0008216493,0.003617979,0.007299394,0.004162849,0.006843281,0.007840388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002011027,"about_ca_system_score_gemma":0.002035043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002708365,"about_ca_topic_score_gemma":0.003255987,"domain_scores_codex":[0.9946897,0.002023961,0.0001555181,0.0007916494,0.001934767,0.0004043504],"domain_scores_gemma":[0.985142,0.00933028,0.0007800806,0.000877056,0.002531579,0.001339096],"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":[0.001197878,0.0002892748,0.0128034,0.003917231,0.000254198,0.003739904,0.001201404,0.001411746,0.02901572,0.08693435,0.1906908,0.6685441],"study_design_scores_gemma":[0.00007076248,0.0006276271,0.002978857,0.003178546,0.0001159892,0.007733138,0.00136943,0.002592365,0.01253638,0.05670794,0.9118985,0.0001905161],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.01777884,0.5406558,0.04383507,0.3118196,0.02395358,0.0002664397,0.001827601,0.001282565,0.05858045],"genre_scores_gemma":[0.3107879,0.386522,0.08125816,0.1492943,0.0279367,0.0004603754,0.002073183,0.0008566172,0.04081082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02006713,"threshold_uncertainty_score":0.06713122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009895140189655514,"score_gpt":0.2546058708446307,"score_spread":0.2447107306549751,"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."}}