{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002376104,0.0001268487,0.0001571917,0.0001305985,0.0004191388,0.00007054473,0.0001372565,0.00008980646,0.00007854257],"category_scores_gemma":[0.0005854576,0.00008607977,0.00005311593,0.0001380506,0.00004181476,0.0001834218,0.00003040631,0.0003817362,0.00005066255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001386278,"about_ca_system_score_gemma":0.0003807164,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02970569,"about_ca_topic_score_gemma":0.1827806,"domain_scores_codex":[0.998913,0.00001692042,0.0002086669,0.0001819921,0.0003172464,0.0003621455],"domain_scores_gemma":[0.9990693,0.0001149212,0.00005644401,0.0002682644,0.0003293008,0.0001617605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004134437,0.0001833884,0.9529824,0.000290341,0.0004160501,0.000002415263,0.001273695,0.0004591368,0.00868489,0.002261047,0.004607756,0.02842543],"study_design_scores_gemma":[0.002387563,0.0001576058,0.9449978,0.0000836574,0.000192818,0.000006490468,0.0003203701,0.01315272,0.0005229297,0.0004052837,0.03757139,0.0002013771],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996901,0.0003584919,0.00006770975,0.0007241729,0.000327231,0.001160119,0.00003796609,0.00005673067,0.000366546],"genre_scores_gemma":[0.9984455,0.00003391205,0.0003705468,0.0004000241,0.0003499411,0.0002327957,0.00007683158,0.00001869078,0.0000717676],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1530749,"threshold_uncertainty_score":0.9767556,"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."}}