{"id":"W3022520316","doi":"10.1101/2020.04.22.20074351","title":"Resilient SARS-CoV-2 diagnostics workflows including viral heat inactivation","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Thomas Hospital","funders":"Biotechnology and Biological Sciences Research Council; Medical Research Council; National Institute of Allergy and Infectious Diseases; European Commission; King's College London; Asthma and Lung UK; Kidney Research UK; National Institute for Health and Care Research; European and Developing Countries Clinical Trials Partnership; Wellcome Trust","keywords":"TaqMan; Virology; Nucleic acid; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); RNA; Viral load; Coronavirus disease 2019 (COVID-19); Primer (cosmetics); RNA extraction; Real-time polymerase chain reaction; Computational biology; Biology; Molecular biology; Virus; Chemistry; Medicine; Gene; Biochemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.004459419,0.001476461,0.001047484,0.00114122,0.0008364641,0.001613557,0.001309264,0.001125353,0.005591958],"category_scores_gemma":[0.004181047,0.001010278,0.001294381,0.000618031,0.0006802094,0.001023547,0.002188351,0.001366125,0.006777989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006712442,"about_ca_system_score_gemma":0.00127363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008907748,"about_ca_topic_score_gemma":0.001381841,"domain_scores_codex":[0.9945384,0.0009335665,0.0005972664,0.001808094,0.001684355,0.0004383031],"domain_scores_gemma":[0.9976974,0.0006141692,0.0002703931,0.0006943246,0.0005903242,0.000133502],"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":[0.0007569596,0.0001611901,0.006082939,0.0007482478,0.0001008542,0.0001743652,0.000455931,0.002721127,0.9460134,0.0004870506,0.004081945,0.03821611],"study_design_scores_gemma":[0.00003750146,0.0005366877,0.008196888,0.0001304887,0.00008722279,0.0006047831,0.0001305094,0.01182654,0.9547211,0.000797046,0.02276245,0.0001688671],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2852311,0.003002199,0.6422222,0.0008888028,0.0008693433,0.002841066,0.009273346,0.04776022,0.007911665],"genre_scores_gemma":[0.414267,0.001856001,0.5465309,0.001344324,0.0002085996,0.004523335,0.01753297,0.005284907,0.008451928],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005591958,"threshold_uncertainty_score":0.02358401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1064336182268711,"score_gpt":0.3490803709341309,"score_spread":0.2426467527072598,"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."}}