{"id":"W3087208335","doi":"10.1101/2020.09.15.20193862","title":"COVID-19 Case Age Distribution: Correction for Differential Testing by Age","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 Clinical Research Studies","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Canadian Blood Services; Public Health Ontario; University of Toronto","funders":"","keywords":"Incidence (geometry); Demography; Medicine; Pandemic; Population; Linear regression; Regression analysis; Negative binomial distribution; Public health; Disease; Statistics; Coronavirus disease 2019 (COVID-19); Environmental health; Internal medicine; Pathology; Mathematics; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007792443,0.0004294945,0.0009984568,0.00007723921,0.0003890119,0.00008997092,0.0002582782,0.0003807531,0.0001657096],"category_scores_gemma":[0.2654752,0.0003803852,0.0004382805,0.0002992242,0.0003238299,0.00002838611,0.001189905,0.001518759,0.00002350939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008698651,"about_ca_system_score_gemma":0.001066926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00082653,"about_ca_topic_score_gemma":0.00008976471,"domain_scores_codex":[0.9967337,0.000196999,0.0007369867,0.001121967,0.0006436193,0.0005667467],"domain_scores_gemma":[0.9863482,0.01116338,0.0002911965,0.0006611344,0.0002740375,0.001262046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004335018,0.002150482,0.1114255,0.03706089,0.002293713,0.08097086,0.001735618,0.0004616736,0.01650619,0.0001840694,0.7004136,0.04246236],"study_design_scores_gemma":[0.01397207,0.003882588,0.03894122,0.002000091,0.003098542,0.0005624989,0.0003535049,0.06639832,0.002155914,0.004896019,0.8612913,0.002447916],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4110632,0.0005061812,0.5148228,0.05248584,0.006549485,0.007733404,0.004982166,0.001396523,0.0004604082],"genre_scores_gemma":[0.990584,0.0001243013,0.0004084946,0.002231162,0.001520884,0.0006838591,0.002623314,0.00006014037,0.001763849],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5795208,"threshold_uncertainty_score":0.9998648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1658744506334478,"score_gpt":0.4521225382355802,"score_spread":0.2862480876021324,"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."}}