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Record W2901726854 · doi:10.1093/cid/ciy978

The Relative Impact of Community and Hospital Antibiotic Use on the Selection of Extended-spectrum Beta-lactamase–producing Escherichia coli

2018· article· en· W2901726854 on OpenAlexaff
Derek R. MacFadden, David N. Fisman, William P. Hanage, Marc Lipsitch

Bibliographic record

VenueClinical Infectious Diseases · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsUniversity of Toronto
FundersNational Institute of General Medical SciencesNational Institute of Allergy and Infectious DiseasesCenters for Disease Control and PreventionNational Institutes of Health
KeywordsAntimicrobial stewardshipMedicineAntibioticsPsychological interventionAntibiotic resistanceAntibiotic StewardshipMedical prescriptionStewardship (theology)Environmental healthIntensive care medicineMicrobiologyNursingBiology

Abstract

fetched live from OpenAlex

Antibiotic stewardship programs have traditionally focused on reducing hospital antibiotic use. However, reducing community antibiotic prescribing could have substantial impacts in both hospital and community settings. We developed a deterministic model of transmission of extended-spectrum beta-lactamase-producing Escherichia coli in both the community and hospitals. We fit the model to existing, national-level antibiotic use and resistance prevalence data from Sweden. Across a range of conditions, a given relative change in antibiotic use in the community had a greater impact on resistance prevalence in both the community and hospitals than an equivalent relative change in hospital use. However, on a per prescription basis, changes in antibiotic use in hospitals had the greatest impact. The magnitude of changes in prevalence were modest, even with large changes in antimicrobial use. These data support the expansion of stewardship programs/interventions beyond the walls of hospitals, but also suggest that such efforts would benefit hospitals themselves.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.321
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations43
Published2018
Admission routes1
Has abstractyes

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