{"id":"W3152824712","doi":"10.1287/inte.2020.1070","title":"The Impact of Age Demographics on Interpreting and Applying Population-Wide Infection Fatality Rates for COVID-19","year":2021,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Pandemic; Government (linguistics); Population; Workforce; Jurisdiction; Health care; Outbreak; Case fatality rate; Coronavirus disease 2019 (COVID-19); Demographics; Business; Public health; Geography; Demography; Political science; Economic growth; Medicine; Disease; Environmental health; Economics; Sociology; Nursing; Virology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0535661,0.000811649,0.0005356677,0.004047985,0.00123555,0.002971136,0.002101464,0.0008145929,0.002059125],"category_scores_gemma":[0.27977,0.0004387826,0.001461511,0.003598916,0.001232813,0.00212223,0.001977683,0.001554844,0.0006165795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003473995,"about_ca_system_score_gemma":0.006222764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3280959,"about_ca_topic_score_gemma":0.332391,"domain_scores_codex":[0.9718056,0.01793947,0.002132959,0.002611788,0.004418574,0.001091478],"domain_scores_gemma":[0.8706861,0.08680879,0.01594632,0.007965434,0.01713512,0.00145838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001630618,0.0000401625,0.9260642,0.0003354972,0.0007353688,0.0002415473,0.00411691,0.008929283,0.0002276036,0.00769037,0.006356432,0.04509964],"study_design_scores_gemma":[0.0000302558,0.0002895845,0.8872819,0.001130339,0.0008166756,0.0007649973,0.01250746,0.03961285,0.002506759,0.01339575,0.04151357,0.0001498441],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7207646,0.01154415,0.1704981,0.0134152,0.001818836,0.001663679,0.02298557,0.0004717563,0.05683815],"genre_scores_gemma":[0.9700627,0.00140882,0.02305149,0.0009803892,0.0002465771,0.0003045498,0.002543813,0.00008635512,0.001315425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3280959,"threshold_uncertainty_score":0.6523724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1729361293571125,"score_gpt":0.4690570621306652,"score_spread":0.2961209327735527,"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."}}