{"id":"W1997009285","doi":"10.1371/journal.pone.0010520","title":"Optimal Pandemic Influenza Vaccine Allocation Strategies for the Canadian Population","year":2010,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Influenza Virus Research Studies","field":"Medicine","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada; Institute for Clinical Evaluative Sciences; Public Health Ontario; University of Toronto","funders":"Ontario Ministry of Research and Innovation; Canadian Institutes of Health Research; Mitacs; University of Toronto; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences; Sanofi","keywords":"Vaccination; Medicine; Pandemic; Transmission (telecommunications); Influenza vaccine; Population; Demography; Epidemiology; Vaccine efficacy; Prioritization; Environmental health; Immunology; Disease; Coronavirus disease 2019 (COVID-19); Internal medicine; Infectious disease (medical specialty); Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009554783,0.0009116186,0.0005826412,0.0006410482,0.001511054,0.001033025,0.001591468,0.0008516167,0.004503243],"category_scores_gemma":[0.003973496,0.0003477023,0.0006678412,0.0005950369,0.0006996188,0.000610389,0.0008586138,0.0006315022,0.0002217288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03452659,"about_ca_system_score_gemma":0.05431432,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9666898,"about_ca_topic_score_gemma":0.9646276,"domain_scores_codex":[0.9995579,0.00009699259,0.000008688379,0.00006276973,0.00006532194,0.0002083055],"domain_scores_gemma":[0.9993585,0.000172337,0.0000609077,0.00001432955,0.0002656782,0.0001282724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000302902,0.0001085783,0.01604131,0.0001570117,0.00009649816,0.0001405031,0.0002233036,0.9517441,0.000919901,0.007969812,0.00754329,0.01475277],"study_design_scores_gemma":[0.0004899144,0.0003481927,0.01735782,0.00006906659,0.0002322831,0.00008641982,0.0008639289,0.9642504,0.0005979231,0.006190987,0.009436636,0.00007646799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.921227,0.001299596,0.02253878,0.004129515,0.0001263134,0.0009942692,0.004370476,0.0002164127,0.04509762],"genre_scores_gemma":[0.9828823,0.0005725109,0.009854238,0.0002983602,0.00001497471,0.0001458956,0.0007862955,0.00001602197,0.005429341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03452659,"threshold_uncertainty_score":0.2505091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1619868745883868,"score_gpt":0.3717651724392957,"score_spread":0.2097782978509089,"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."}}