{"id":"W3025706910","doi":"10.1101/2020.05.14.097287","title":"Recommendations for sample pooling on the Cepheid GeneXpert <sup>®</sup> system using the Cepheid Xpert <sup>®</sup> Xpress SARS-CoV-2 assay","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Public Health Agency of Canada","funders":"","keywords":"Pooling; GeneXpert MTB/RIF; Virology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Point of care; Pandemic; Coronavirus disease 2019 (COVID-19); Point-of-care testing; Population; Statistics; Medicine; Computer science; Mathematics; Environmental health; Immunology; Infectious disease (medical specialty); Internal medicine; Disease; Artificial intelligence; Nursing; Tuberculosis","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.002370069,0.001350168,0.001458479,0.0004198581,0.001624359,0.0007230217,0.001307213,0.0009106635,0.00004243618],"category_scores_gemma":[0.004130922,0.001028162,0.0007901569,0.001216646,0.0002581084,0.0002283812,0.0007920638,0.002221814,0.00008813021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001237849,"about_ca_system_score_gemma":0.001060546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006233655,"about_ca_topic_score_gemma":0.000005427062,"domain_scores_codex":[0.9932379,0.0008241068,0.001597182,0.002046323,0.0009850718,0.001309437],"domain_scores_gemma":[0.9924374,0.00223866,0.001007762,0.002837697,0.001184558,0.0002939355],"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":[0.0002969593,0.0002851628,0.0007956288,0.001122155,0.001093376,0.00005816652,0.0005606262,0.002183524,0.9871085,0.0014045,0.005052411,0.00003900502],"study_design_scores_gemma":[0.001415338,0.0001608915,0.0001498509,0.002314465,0.0006893791,0.000001811056,0.0004844891,0.2615834,0.6734126,0.000007659024,0.05867383,0.001106285],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9343761,0.001090147,0.04571785,0.007293535,0.002149571,0.006077178,0.001138365,0.002054257,0.000103036],"genre_scores_gemma":[0.9651614,0.00005402925,0.0200455,0.009600775,0.003348787,0.0012654,0.000007033318,0.000512404,0.000004612786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3136958,"threshold_uncertainty_score":0.999925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09431658928367072,"score_gpt":0.3011296286052466,"score_spread":0.2068130393215759,"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."}}