{"id":"W4301391002","doi":"10.1111/trf.17140","title":"How do we decide how representative our donors are for public health surveillance?","year":2022,"lang":"en","type":"article","venue":"Transfusion","topic":"Blood donation and transfusion practices","field":"Business, Management and Accounting","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Sherbrooke; Héma-Québec; University of Alberta; Canadian Blood Services; University of Ottawa","funders":"Canadian Blood Services","keywords":"Population; Medicine; Representativeness heuristic; Pandemic; Public health; Seroprevalence; Public health surveillance; Environmental health; Disease; Infectious disease (medical specialty); Immunology; Serology; Coronavirus disease 2019 (COVID-19); Internal medicine; Pathology; Statistics","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":[0.001330897,0.0002334878,0.0003283355,0.0004022063,0.001273786,0.0008773185,0.0003435475,0.00005215233,0.0003419927],"category_scores_gemma":[0.0001294629,0.0002241892,0.0001942685,0.0008845317,0.00002460633,0.0018765,0.00009236869,0.0003015046,0.00001471092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006251227,"about_ca_system_score_gemma":0.00007273609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000474643,"about_ca_topic_score_gemma":0.001672123,"domain_scores_codex":[0.9979817,0.0001226158,0.0002637663,0.0005742771,0.0006072419,0.000450378],"domain_scores_gemma":[0.9988678,0.0001501395,0.0004002499,0.0003008034,0.0002332933,0.00004776786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001827856,0.002630478,0.147394,0.001734199,0.0004855141,0.00007678931,0.004612214,0.0003556355,0.002485337,0.08098616,0.2751445,0.4822673],"study_design_scores_gemma":[0.002865557,0.00006685088,0.0109676,0.00002243917,0.00002923078,0.000007662336,0.02709884,0.0005955139,0.00005858707,0.00173934,0.9561917,0.000356687],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1386844,0.001037032,0.01563137,0.8391656,0.0016523,0.001731076,0.0001083229,0.0003941506,0.001595785],"genre_scores_gemma":[0.9925658,0.001033917,0.0003702043,0.00367159,0.0005180999,0.0003095633,0.0001404446,0.00005150588,0.001338929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8538814,"threshold_uncertainty_score":0.9797059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06337515717244441,"score_gpt":0.2890731299628151,"score_spread":0.2256979727903707,"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."}}