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Record W2026307338 · doi:10.1016/j.java.2012.10.003

Evaluation of a Luer-Activated Intravenous Administration System

2012· article· en· W2026307338 on OpenAlexaff
Crystal Edwards, Chad Johnson

Bibliographic record

VenueJournal of the Association for Vascular Access · 2012
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsThunder Bay Regional Health Sciences Centre
Fundersnot available
KeywordsMedicineNursingMedical emergencyAdministration (probate law)Needlestick injuryFamily medicine

Abstract

fetched live from OpenAlex

Abstract Needlestick and other sharps-related injuries are largely preventable with proper education, training, and the use of safety-engineered devices. In 2009, a review of clinical practice was completed at Thunder Bay Regional Health Sciences Centre. The review revealed that despite needle-free legislation, numerous years of education on the dangers of using needles, and the availability and importance of using safety devices, nurses and physicians continued to use needles when accessing intravenous tubing to administer medication. During 2010, a luer-activated intravenous administration system was introduced to replace the current split-septum intravenous administration system. Implementation of the luer-activated system was expected to decrease needlestick injuries, positively affect nursing practice, and demonstrate a commitment to a safe working environment. Reported needlestick injuries were reviewed and analysed pre- and post-implementation and a survey on nurse perception of the new system and organizational safety was distributed. Results showed that there was a 46% decrease in needlestick injuries post-implementation, along with 80% of nursing staff reporting that the new system had a positive influence on their nursing practice and belief that the organization was committed to providing a safe work environment. The results of this study emphasize and support the replacement of needles with alternative needleless products to improve the safety of the work environment.

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)0.008
metaresearch head score (Gemma)0.004
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 score0.071
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.062
GPT teacher head0.383
Teacher spread0.321 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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