{"id":"W3092298165","doi":"10.1186/s12992-020-00623-x","title":"Lessons learned from COVID-19 for the post-antibiotic future","year":2020,"lang":"en","type":"article","venue":"Globalization and Health","topic":"Antibiotic Use and Resistance","field":"Immunology and Microbiology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Global Health Research; Global Affairs Canada; McMaster University; York University; University of Ottawa","funders":"","keywords":"Scarcity; Global health; Health care; Social policy; Economic growth; Pandemic; Development economics; Political science; Public relations; Coronavirus disease 2019 (COVID-19); Business; Economics; Medicine; Infectious disease (medical specialty)","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":[0.01350686,0.0009640937,0.0008371598,0.0009486629,0.003866433,0.008695081,0.002170067,0.01166701,0.02428065],"category_scores_gemma":[0.02304776,0.0002854502,0.001384064,0.0007159021,0.005105723,0.009151462,0.006619822,0.0171268,0.00582651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004105802,"about_ca_system_score_gemma":0.02184241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00724545,"about_ca_topic_score_gemma":0.01610061,"domain_scores_codex":[0.9940757,0.002492667,0.0002957071,0.0004788885,0.00104093,0.001616206],"domain_scores_gemma":[0.9721268,0.007779847,0.0009780036,0.0008979318,0.004682678,0.0135348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000129354,0.0002242757,0.002133867,0.001242994,0.00002762691,0.001411652,0.002975233,0.0002663208,0.0003378282,0.03148703,0.831602,0.1281618],"study_design_scores_gemma":[0.00002898568,0.0001964812,0.002292875,0.003865965,0.00001972131,0.001025302,0.01308084,0.0001769607,0.0003011108,0.03758867,0.9413556,0.00006751931],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.001415817,0.02305693,0.0005816114,0.9516058,0.01603375,0.0000204582,0.0001215089,0.0000605727,0.007103522],"genre_scores_gemma":[0.09338462,0.1123797,0.00852199,0.7306505,0.02991429,0.0001954813,0.0006058943,0.000141505,0.02420593],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.02428065,"threshold_uncertainty_score":0.08122683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07824643351191446,"score_gpt":0.3527061391291158,"score_spread":0.2744597056172013,"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."}}