{"id":"W3016007874","doi":"10.1101/2020.04.06.028902","title":"Optimization of SARS-CoV-2 detection by RT-QPCR without RNA extraction","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Trois-Rivières; Centre intégré universitaire de santé et de services sociaux de la Mauricie-et-du-Centre-du-Québec","funders":"","keywords":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); RNA extraction; Extraction (chemistry); RNA; Detection limit; Coronavirus disease 2019 (COVID-19); Virology; Real-time polymerase chain reaction; 2019-20 coronavirus outbreak; Biology; Chromatography; Chemistry; Medicine; Gene; Biochemistry; Pathology","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":[0.003867401,0.002079399,0.001267351,0.000769606,0.0004908438,0.001123232,0.0009828883,0.001143181,0.003465215],"category_scores_gemma":[0.003536355,0.001172418,0.0008824692,0.0005816732,0.001206382,0.000577465,0.0006023027,0.001428542,0.004388643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004392542,"about_ca_system_score_gemma":0.0006557583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006048224,"about_ca_topic_score_gemma":0.001427055,"domain_scores_codex":[0.9944536,0.001653185,0.0004034129,0.001326663,0.001740369,0.0004227017],"domain_scores_gemma":[0.998209,0.0009925707,0.0001529056,0.0002004177,0.0003937761,0.00005131581],"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":[0.00007520983,0.00005333838,0.0002028918,0.0001117447,0.000007379677,0.00002018971,0.0000435358,0.0004124602,0.9970493,0.00007357334,0.0001122645,0.001838046],"study_design_scores_gemma":[0.000009744403,0.0002642167,0.0007755528,0.00001910276,0.00002513806,0.0000538661,0.00001911706,0.002334914,0.9934555,0.00008275873,0.002934412,0.0000257027],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4564368,0.003659382,0.5205986,0.0005488549,0.0008712591,0.003443382,0.004154658,0.004078055,0.006208979],"genre_scores_gemma":[0.4171842,0.00280915,0.5474979,0.0006873389,0.0001938939,0.005921718,0.009020973,0.001675428,0.01500927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003867401,"threshold_uncertainty_score":0.02045304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03789329871536536,"score_gpt":0.2808001922569067,"score_spread":0.2429068935415414,"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."}}