{"id":"W3015170908","doi":"10.1371/journal.pone.0238904","title":"Monitoring trends and differences in COVID-19 case-fatality rates using decomposition methods: Contributions of age structure and age-specific fatality","year":2020,"lang":"en","type":"article","venue":"PLoS ONE","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":153,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council; H2020 European Research Council","keywords":"Case fatality rate; Demography; Age structure; Coronavirus disease 2019 (COVID-19); Population; Medicine; Geography; Age groups; Disease; Infectious disease (medical specialty); Pathology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006407748,0.0001773025,0.0007808422,0.0000603181,0.0001868402,0.00003876066,0.00007895373,0.0001096224,0.00002162754],"category_scores_gemma":[0.005434984,0.0001455831,0.00004211468,0.0002725169,0.0002426361,0.000103847,0.0001967908,0.000213628,1.016558e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000125832,"about_ca_system_score_gemma":0.00001485896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004262527,"about_ca_topic_score_gemma":0.0001034391,"domain_scores_codex":[0.9981438,0.0006688844,0.0004861371,0.0003543612,0.0001464832,0.0002003641],"domain_scores_gemma":[0.9952959,0.00413119,0.0002030585,0.0001514379,0.00005183422,0.0001665704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002209858,0.0007237665,0.722381,0.002963388,0.0005805189,0.0005536256,0.006278078,0.00001929973,0.2598807,0.003636912,0.00003190556,0.002729843],"study_design_scores_gemma":[0.00299845,0.0003693618,0.6706445,0.0009315059,0.0008314029,0.00006138228,0.00253263,0.006113773,0.1009964,0.2135765,0.00002989747,0.0009141995],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861398,0.001526115,0.01051365,0.001216498,0.00001290903,0.0002179473,0.0003146898,0.00005216577,0.000006228291],"genre_scores_gemma":[0.938701,0.0002685554,0.06085469,0.00008487085,0.00006013308,0.00001034104,0.00001234574,0.000006924484,0.00000118493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2099396,"threshold_uncertainty_score":0.6506576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5607372143281104,"score_gpt":0.497364428085187,"score_spread":0.06337278624292342,"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."}}