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Record W2914810136 · doi:10.1097/nur.0000000000000426

A Multifaceted Approach to the Prevention of Clostridioides (Clostridium) Difficile

2019· article· en· W2914810136 on OpenAlexaff
Kimberly Pate, Jennifer Reece, Alex Smyre

Bibliographic record

VenueClinical Nurse Specialist · 2019
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsKimberly-Clark (Canada)
Fundersnot available
KeywordsClostridioidesClostridium difficileClostridiumMedicineClostridium InfectionsIntensive care medicineMicrobiologyInternal medicineBiologyBacteria

Abstract

fetched live from OpenAlex

PURPOSE/OBJECTIVES: The purpose of this project was to design and implement a sustainable program to reduce hospital-acquired cases of Clostridioides difficile. DESCRIPTION OF THE PROJECT: Experiencing higher rates in a large, academic medical center, hospital leaders were assembled. The overall facility rate was 6.9% in 2014 with a first quarter rate of 8.4% in 2015. Individual unit rates were as high as 19.8%. A team of key stakeholders was assembled to plan, execute, and reevaluate targeted solutions. Strategies implemented were an innovative, automated screening tool, an evidence-based prevention bundle; and staff education. OUTCOMES: A facility-wide C difficile prevention program was implemented with a sustained decrease in rates observed from 8.4% in the first quarter of 2015 to 6.0% in the fourth quarter of 2017. The standardized infection ratio ranged from 0.541 to 0.889, consistently below the national mean. CONCLUSION: Clostridioides difficile is a leading cause of hospital-associated diarrhea and a tremendous burden on healthcare systems increasing morbidity, mortality, and financial strain. A multidisciplinary, multifaceted approach was critical to ensure early detection, reduce risk of transmission, and decrease overall rates.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.063
GPT teacher head0.383
Teacher spread0.320 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations6
Published2019
Admission routes1
Has abstractyes

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