{"id":"W3214944786","doi":"10.1017/ice.2021.480","title":"Leveraging implementation science to advance antibiotic stewardship practice and research","year":2021,"lang":"en","type":"article","venue":"Infection Control and Hospital Epidemiology","topic":"Antibiotic Use and Resistance","field":"Immunology and Microbiology","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"U.S. Department of Veterans Affairs","keywords":"Stewardship (theology); Content (measure theory); Antibiotic Stewardship; Action (physics); Call to action; Computer science; World Wide Web; Business; Antibiotics; Political science; Microbiology; Advertising; Biology; Mathematics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2294826,0.001512177,0.002031139,0.005330655,0.002389084,0.01847382,0.0042272,0.007589098,0.01812249],"category_scores_gemma":[0.3368584,0.0009845734,0.002124588,0.003969071,0.01162917,0.01711093,0.01370525,0.01368867,0.002756925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01278037,"about_ca_system_score_gemma":0.08326688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004575714,"about_ca_topic_score_gemma":0.007986729,"domain_scores_codex":[0.8224037,0.1491848,0.00538494,0.00362971,0.01450949,0.004887416],"domain_scores_gemma":[0.4792307,0.429279,0.01288334,0.03154388,0.03000457,0.0170585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002797084,0.001290816,0.01217846,0.007303984,0.000634721,0.0001884389,0.00334261,0.00631392,0.0006132877,0.4122823,0.07549619,0.4800756],"study_design_scores_gemma":[0.0003688214,0.0009496869,0.006217647,0.01521847,0.0003036699,0.0001542019,0.005186764,0.008838668,0.001574301,0.825017,0.1360019,0.0001688839],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.007485135,0.01938598,0.1600871,0.7485294,0.004988109,0.001147135,0.0004717897,0.0007250467,0.05718037],"genre_scores_gemma":[0.4588293,0.05346523,0.3638035,0.1035818,0.008259682,0.003545596,0.0008781085,0.0005759545,0.007060973],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2294826,"threshold_uncertainty_score":0.9501851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03474144392191426,"score_gpt":0.3963934432850962,"score_spread":0.361651999363182,"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."}}