{"id":"W3201089801","doi":"10.7554/elife.68764","title":"Microbiome-pathogen interactions drive epidemiological dynamics of antibiotic resistance: A modeling study applied to nosocomial pathogen control","year":2021,"lang":"en","type":"article","venue":"eLife","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Université de Versailles Saint-Quentin-en-Yvelines; Canadian Institutes of Health Research; Université Paris-Saclay; Institut National de la Santé et de la Recherche Médicale; Agence Nationale de la Recherche","keywords":"Microbiome; Antibiotic resistance; Antibiotics; Colonisation resistance; Biology; Microbiology; Infection control; Population; Antimicrobial stewardship; Dysbiosis; Epidemiology; Pathogen; Medicine; Intensive care medicine; Environmental health; Bioinformatics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004016821,0.0001989453,0.0004404146,0.00005815664,0.0001193344,0.00001569331,0.0001946521,0.0001572438,0.00001842996],"category_scores_gemma":[0.0001968069,0.0001895103,0.0001429505,0.0001689463,0.00004086175,0.000002566994,0.0001625682,0.0001657421,0.00002000241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005848229,"about_ca_system_score_gemma":0.0001906775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000018029,"about_ca_topic_score_gemma":0.0007115143,"domain_scores_codex":[0.9982935,0.000202034,0.0005201509,0.000544661,0.0001036314,0.0003360689],"domain_scores_gemma":[0.9989946,0.00003928587,0.0001315897,0.0004477555,0.0002423736,0.000144398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002330484,0.0003717254,0.003640655,0.00002815008,0.00009298155,0.00001212924,0.0001560018,0.0007781262,0.9934433,0.000166571,0.0009404982,0.0001368532],"study_design_scores_gemma":[0.02722036,0.004701402,0.09553798,0.0006844559,0.001280537,0.0002657944,0.01727445,0.01222443,0.7806378,0.0004492056,0.05474509,0.004978512],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9752247,0.0002937197,0.02218282,0.000624996,0.0002014413,0.0006353085,0.0004352118,0.00001615048,0.0003856274],"genre_scores_gemma":[0.993952,0.00005498083,0.004054174,0.001043126,0.0001990651,0.00001955889,0.0004188787,0.00002389799,0.0002342595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2128055,"threshold_uncertainty_score":0.7728007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01532443512870068,"score_gpt":0.2943399802030587,"score_spread":0.279015545074358,"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."}}