{"id":"W2106447438","doi":"10.1093/heapol/czq026","title":"Strengthening the International Health Regulations: lessons from the H1N1 pandemic","year":2010,"lang":"en","type":"article","venue":"Health Policy and Planning","topic":"Viral Infections and Outbreaks Research","field":"Medicine","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"U.S. National Library of Medicine; National Institutes of Health","keywords":"International Health Regulations; Public health; Pandemic; Global health; Business; International health; Scope (computer science); Corporate governance; Health policy; Economic growth; Political science; Environmental health; Medicine; Disease; Coronavirus disease 2019 (COVID-19); Infectious disease (medical specialty); Economics","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0257479,0.0007313989,0.0007307603,0.001348972,0.00303719,0.007320581,0.002004579,0.007918824,0.004638622],"category_scores_gemma":[0.03229749,0.000380488,0.001068534,0.001481904,0.009951916,0.01202339,0.004941585,0.0113308,0.000867115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005176289,"about_ca_system_score_gemma":0.02572817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0185632,"about_ca_topic_score_gemma":0.02266355,"domain_scores_codex":[0.9906514,0.005734032,0.0003623183,0.0003687973,0.001615233,0.001268242],"domain_scores_gemma":[0.9673865,0.02113295,0.001629966,0.001747706,0.00437488,0.003728085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001520298,0.0002814233,0.00343854,0.00120479,0.00006097948,0.0005885403,0.006685188,0.002135171,0.0002611306,0.382926,0.3991657,0.2031004],"study_design_scores_gemma":[0.0001133167,0.0002020968,0.004662937,0.003192247,0.00005249648,0.0002579777,0.01262663,0.0005957854,0.00040814,0.2239197,0.7538739,0.00009471615],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.004118409,0.05602057,0.001863403,0.8943017,0.006050233,0.00003576154,0.0001167553,0.00005294369,0.0374402],"genre_scores_gemma":[0.2896347,0.2254242,0.01123392,0.4355248,0.01944934,0.0002479999,0.0004273978,0.000113681,0.01794386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0257479,"threshold_uncertainty_score":0.1361696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1280965442180355,"score_gpt":0.4851065162038816,"score_spread":0.3570099719858461,"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."}}