{"id":"W4311203997","doi":"10.1017/dmp.2022.281","title":"Biases in COVID-19 Case and Death Definitions: Potential Causes and Consequences","year":2022,"lang":"en","type":"article","venue":"Disaster Medicine and Public Health Preparedness","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Public health; China; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Public health surveillance; Disease; Medicine; Criminology; Data science; Political science; Infectious disease (medical specialty); Virology; Psychology; Computer science; Law; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.4308753,0.000798894,0.001393305,0.007222243,0.002390062,0.004747321,0.003942295,0.002215679,0.002198127],"category_scores_gemma":[0.7151,0.0008661303,0.001298108,0.007304071,0.006615913,0.007187061,0.00514438,0.003170991,0.0004291805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004073872,"about_ca_system_score_gemma":0.004311834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006978548,"about_ca_topic_score_gemma":0.00691456,"domain_scores_codex":[0.4807973,0.4242345,0.04078707,0.01437904,0.0370758,0.002726296],"domain_scores_gemma":[0.1674764,0.6637455,0.09239457,0.03743274,0.03766011,0.001290629],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001320648,0.0001663252,0.6301956,0.005508021,0.001769482,0.0007132747,0.03395348,0.001961991,0.0004211936,0.1011215,0.02122951,0.2016389],"study_design_scores_gemma":[0.0005668064,0.001265005,0.4466212,0.03549298,0.002845153,0.00716872,0.04018543,0.01672403,0.01038155,0.3096616,0.1279981,0.001089391],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.415742,0.07395174,0.2447663,0.1977708,0.007486292,0.005456271,0.007549384,0.0002905113,0.0469867],"genre_scores_gemma":[0.9113539,0.007311126,0.04697604,0.02731518,0.002675887,0.001829179,0.001324692,0.0001156983,0.001098331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5691247,"threshold_uncertainty_score":0.7018321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.619223959970045,"score_gpt":0.5027846056456422,"score_spread":0.1164393543244028,"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."}}