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Record W3155297503 · doi:10.12968/bjon.2021.30.8.s37

Use of dry dressings for central venous access devices (CVADs) to decrease central line-associated blood stream infections (CLABSI) in a trauma intensive care unit (ICU)

2021· article· en· W3155297503 on OpenAlexaff
France Paquet, Janette Morlese, Charles Frenette

Bibliographic record

VenueBritish Journal of Nursing · 2021
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsAthabasca UniversityUniversité de MontréalMcGill University Health Centre
Fundersnot available
KeywordsMedicineCentral lineBlood streamBloodstream infectionCentral venous catheterIntensive care unitIntensive care medicineCatheterAuditEmergency medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

This article reports the results of a pre-post study conducted in a trauma-medical-surgical intensive care unit (ICU) regarding dressings of central venous access devices (CVADs) for the reduction of central line-associated blood stream infection (CLABSI) and improvement of adherence and integrity of the dressing. Available evidence indicates that dry dressings changed every 48 hours are equivalent to transparent dressings, changed when soiled or loose, or routinely every seven days. In our intensive care unit, where the majority of CVADs are inserted in the internal jugular vein and where there is an important usage of cervical collars, we questioned if dry dressings would be more appropriate than transparent dressings. Results: In the 12 months following the change in practice, we noted a CLABSI reduction from 2.36/1,000 catheter days to zero, improvement in dressing audits from 19.61% to 85.34% of clean dressings (P=0.00001) and 62.75% to 90.58% of adherent dressings. Conclusion: In this pre-post study, a simple change in dressing type was implemented, resulting in a significant reduction in the CLABSI rate.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.310
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.121
GPT teacher head0.405
Teacher spread0.285 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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