{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2799394,0.000675811,0.002640433,0.003967102,0.002842307,0.01099635,0.004988124,0.0046214,0.001976967],"category_scores_gemma":[0.481893,0.0007295676,0.001244871,0.004174833,0.006911725,0.01309651,0.004430866,0.005408177,0.00152556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004256321,"about_ca_system_score_gemma":0.008366536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007701169,"about_ca_topic_score_gemma":0.00606694,"domain_scores_codex":[0.7934346,0.155721,0.01899001,0.00868501,0.01997465,0.003194854],"domain_scores_gemma":[0.6141995,0.219845,0.04474379,0.03084938,0.07819578,0.01216657],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007789289,0.0003855874,0.4922403,0.002725737,0.001139279,0.0004515767,0.01415328,0.002221624,0.001146628,0.03212491,0.0980476,0.3545846],"study_design_scores_gemma":[0.0003988318,0.001502921,0.3124922,0.02711431,0.001739487,0.003361729,0.07044832,0.01380192,0.007568251,0.1856605,0.3752533,0.000658372],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2842589,0.06227565,0.1272822,0.4784286,0.01124789,0.002396667,0.005689322,0.0004278442,0.02799286],"genre_scores_gemma":[0.7948124,0.01539322,0.1163776,0.06102888,0.003753735,0.004846909,0.002166486,0.0002536113,0.00136716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7200606,"threshold_uncertainty_score":0.8879629,"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."}}