{"id":"W3092282932","doi":"10.1093/geront/gnaa152","title":"National Profiles of Coronavirus Disease 2019 Mortality Risks by Age Structure and Preexisting Health Conditions","year":2020,"lang":"en","type":"article","venue":"The Gerontologist","topic":"COVID-19 and healthcare impacts","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute on Aging; Social Sciences and Humanities Research Council of Canada; Government of Canada; National Institute of Child Health and Human Development; Canadian Institutes of Health Research; Pennsylvania State University; University of Pennsylvania","keywords":"Coronavirus disease 2019 (COVID-19); Coronavirus; Disease; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Environmental health; Pandemic; Medicine; Virology; Outbreak; Infectious disease (medical specialty); Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001923464,0.0001038684,0.0002534799,0.00001463725,0.0001622101,0.00001057821,0.00007532515,0.00004616589,0.0001035792],"category_scores_gemma":[0.0006269183,0.00007095245,0.00003634602,0.00006525734,0.0003263444,0.00004079126,0.00003995226,0.0001896964,0.000002095423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000669428,"about_ca_system_score_gemma":0.0005664633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002985344,"about_ca_topic_score_gemma":0.001114557,"domain_scores_codex":[0.9990156,0.0000991287,0.0002504362,0.0001859221,0.0002606898,0.0001882339],"domain_scores_gemma":[0.9991193,0.0001633417,0.0001766963,0.0001533733,0.00006837697,0.0003189106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009538434,0.0002112059,0.920426,0.002532879,0.0001958759,0.00005597123,0.003394088,0.0000570685,0.001985315,0.00599181,0.05418935,0.01000663],"study_design_scores_gemma":[0.0005493692,0.0002165485,0.9954353,0.00004797827,0.00004545939,0.000006931848,0.00009047366,0.0001579244,0.00006778596,0.001246959,0.002069187,0.00006609852],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9501492,0.003020898,0.0001626329,0.04103218,0.00005397035,0.000735364,0.004528555,0.00005977598,0.0002573617],"genre_scores_gemma":[0.9925213,0.00008660676,0.00007409806,0.006596867,0.0001031532,0.000009138093,0.0005421908,0.000007611518,0.00005899005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07500932,"threshold_uncertainty_score":0.4512966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3113104013557124,"score_gpt":0.4925829769291183,"score_spread":0.1812725755734059,"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."}}