{"id":"W2029456013","doi":"10.1371/currents.rrn1144","title":"Optimal Pandemic Influenza Vaccine Allocation Strategies for the Canadian Population","year":2010,"lang":"en","type":"article","venue":"PLoS Currents","topic":"Influenza Virus Research Studies","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Institute for Clinical Evaluative Sciences; Public Health Agency of Canada","funders":"Ontario Ministry of Research and Innovation; Department of Family and Community Medicine, University of Toronto; Canadian Institutes of Health Research; Mitacs; University of Toronto; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences","keywords":"Vaccination; Medicine; Pandemic; Transmission (telecommunications); Prioritization; Influenza vaccine; Population; Epidemiology; Demography; Age groups; Environmental health; Immunology; Coronavirus disease 2019 (COVID-19); Disease; Internal medicine; Infectious disease (medical specialty)","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":[0.0009987478,0.0009712569,0.0006427416,0.0006589715,0.001536484,0.001038954,0.00163814,0.0008788761,0.004522121],"category_scores_gemma":[0.004102958,0.0003889948,0.0007186523,0.0006342049,0.000707493,0.0006280844,0.0008526954,0.0006633862,0.0002314241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03866835,"about_ca_system_score_gemma":0.05700214,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9689798,"about_ca_topic_score_gemma":0.966319,"domain_scores_codex":[0.9995015,0.0001095009,0.000009953305,0.00007421256,0.00007343621,0.0002314322],"domain_scores_gemma":[0.9993054,0.0001839476,0.00006711757,0.0000164586,0.0002920583,0.000135009],"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":[0.0003193698,0.0001065372,0.01566272,0.0001670127,0.0001011931,0.000133058,0.0002073438,0.9513756,0.001011941,0.007828556,0.0076862,0.01540037],"study_design_scores_gemma":[0.0004907758,0.0003732952,0.01863611,0.00007252438,0.0002681408,0.00008799352,0.0007669564,0.9621437,0.0006658173,0.006562692,0.00984669,0.00008517066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9139959,0.001569444,0.02677415,0.004490261,0.0001373759,0.001154268,0.005343711,0.0002666794,0.04626816],"genre_scores_gemma":[0.9812625,0.0006491658,0.01094676,0.0003092183,0.00001579311,0.0001604843,0.0008979712,0.00001811182,0.005740007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03866835,"threshold_uncertainty_score":0.2805598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1404837773801884,"score_gpt":0.4139431097942479,"score_spread":0.2734593324140595,"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."}}