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Record W2145696927 · doi:10.14740/jocmr2314w

The Impact of Implementation of Bundle to Reduce Catheter-Related Bloodstream Infection Rates

2015· article· en· W2145696927 on OpenAlexvenueno aff
Mayra Gonçalves Menegüeti, Kym Ardison, Fernando Bellissimo‐Rodrigues, Gilberto Gambero Gaspar, Olindo Assis Martins‐Filho, Marcelo Lourencini Puga, Ana Maria Laüs, Aníbal Basile-Filho, Maria Auxiliadora‐Martins

Bibliographic record

VenueJournal of Clinical Medicine Research · 2015
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineChecklistCentral venous catheterBloodstream infectionBundleCatheterIncidence (geometry)Infection controlEmergency medicineIntensive care medicineCritically illSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of the study was to investigate how control bundles reduce the rate of central venous catheter-associated bloodstream infections (CVC-BSIs) rates in critically ill patients. METHODS: This is a prospective before-and-after study designed to evaluate whether a set of control measures (bundle) can help prevent CVC-BSI. The bundles included a checklist that aimed to correct practices related to CVC insertion, manipulation, and maintenance based on guidelines of the Center for Disease Control and Prevention (CDC). RESULTS: We examined 123 checklists before and 155 checklists after implementation of the training program. Compared with the pre-intervention period, CVC-BSI rates decreased. Hand hygiene techniques were used correctly. CVC-BSI incidence was 9.3 and 5.1 per 1,000 catheter-days before and after the training program, respectively. CONCLUSIONS: The implementation of a bundle and training program effectively reduces CVC-BSI 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)0.024
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.419
GPT teacher head0.678
Teacher spread0.259 · 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.

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

Citations30
Published2015
Admission routes1
Has abstractyes

Explore more

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