{"id":"W4212924273","doi":"10.1101/2022.02.17.22271099","title":"Timeliness of reporting of SARS-CoV-2 seroprevalence results and their utility for infectious disease surveillance","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; McMaster University; University of New Brunswick; University of Toronto; Hamilton Health Sciences; Canada Research Chairs; McGill University; University of Calgary","funders":"Canadian Medical Association; Robert Koch Institut; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology; Public Health Agency; Public Health Agency of Canada; World Health Organization","keywords":"Seroprevalence; Representativeness heuristic; Medicine; Population; Sampling frame; Sample (material); Univariate; Demography; Environmental health; Family medicine; Statistics; Multivariate statistics; Serology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2542229,0.0009646082,0.00245595,0.01848449,0.000949218,0.00524516,0.002385975,0.001280465,0.003283988],"category_scores_gemma":[0.6066086,0.001222934,0.007462893,0.02564517,0.001602837,0.00636131,0.00354701,0.001472904,0.0004758545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002620039,"about_ca_system_score_gemma":0.006011047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00500186,"about_ca_topic_score_gemma":0.006252415,"domain_scores_codex":[0.5787602,0.1826338,0.1855133,0.01482085,0.03576256,0.002509317],"domain_scores_gemma":[0.1165011,0.5506537,0.2641404,0.03205234,0.03519109,0.001461433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003349204,0.00008708407,0.752026,0.07400657,0.02382069,0.0003712438,0.004471757,0.002012873,0.000834711,0.001540784,0.008125199,0.129354],"study_design_scores_gemma":[0.0007121267,0.001530019,0.8456904,0.06958081,0.01879593,0.002464394,0.004052933,0.004756947,0.002735295,0.003164617,0.04610136,0.0004151628],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.4026884,0.4619164,0.03116208,0.01266276,0.002837567,0.003849994,0.07604051,0.0005970824,0.008245301],"genre_scores_gemma":[0.9272009,0.03647347,0.01600807,0.001109254,0.001066625,0.004117471,0.01310323,0.000231096,0.0006898962],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2542229,"threshold_uncertainty_score":0.9196759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07209403389258424,"score_gpt":0.3538562999928234,"score_spread":0.2817622661002391,"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."}}