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Record W2156304358 · doi:10.1093/cid/ciu445

Hospital-wide Rollout of Antimicrobial Stewardship: A Stepped-Wedge Randomized Trial

2014· article· en· W2156304358 on OpenAlexafffund
Lesley Palmay, Marion Elligsen, Sandra A N Walker, Ruxandra Pinto, TR Einarson, A. E. Simor, Anita Rachlis, Samira Mubareka, Nick Daneman

Bibliographic record

VenueClinical Infectious Diseases · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsSunnybrook HospitalUniversity of TorontoHealth Sciences CentreInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
FundersCanadian Institutes of Health Research
KeywordsMedicineAntimicrobial stewardshipRandomized controlled trialPsychological interventionAuditIntensive care medicineEmergency medicineIntervention (counseling)Stewardship (theology)Intensive care unitAntibioticsNursingAntibiotic resistanceSurgeryAccounting

Abstract

fetched live from OpenAlex

Our objective was to rigorously evaluate the impact of an antimicrobial stewardship audit-and-feedback intervention, via a stepped-wedge randomized trial. An effective intensive care unit (ICU) audit-and-feedback program was rolled out to 6 non-ICU services in a randomized sequence. The primary outcome was targeted antimicrobial utilization, using a negative binomial regression model to assess the impact of the intervention while accounting for secular and seasonal trends. The intervention was successfully transitioned, with high volumes of orders reviewed, suggestions made, and recommendations accepted. Among patients meeting stewardship review criteria, the intervention was associated with a large reduction in targeted antimicrobial utilization (-21%, P = .004); however, there was no significant change in targeted antibiotic use among all admitted patients (-1.2%, P = .9), and no reductions in overall costs and microbiologic outcomes. An ICU day 3 audit-and-feedback program can be successfully expanded hospital-wide, but broader benefits on non-ICU wards may require interventions earlier in the course of treatment.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.290
Teacher spread0.279 · 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 designRandomized trial
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

Citations51
Published2014
Admission routes2
Has abstractyes

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