Closed system for the safe management of intravenous fluids and the reduction of healthcare-associated bloodstream infections
Bibliographic record
Abstract
Introduction: the use of closed infusion systems for the administration of intravenous fluids has shown a notable impact on reducing Central Line-Associated Bloodstream Infections (CLABSI); however, their adoption in Chile remains limited. Objective: This study aimed to assess the role of closed infusion systems in preventing CLABSI. Methods: a rapid literature review was conducted, consulting databases such as Web of Science, SCOPUS, PubMed, SciELO, CINAHL, the Cochrane Library, and BVS. The searches were carried out in the Spanish, English, and Portuguese languages; only studies with a quantitative approach were included, with no time limit and that answered the research question. The Canadian Task Force on Preventive Health Care criteria were utilized to analyze the level of evidence and grade of recommendation. Results: out of the nine articles reviewed, 11 % presented Level IA evidence, and 88,9 % presented evidence and a recommendation grade of IIB, indicating that patients receiving intravenous fluids through open containers are at a two to five times higher risk of developing CLABSI compared to those using closed systems. Conclusion: the employment of closed systems for the administration of intravenous fluids via CVC is associated with a significantly lower risk of acquiring CLABSI compared to the use of open systems. This finding underscores the necessity of promoting the use of closed infusion technologies as a preventative measure in the clinical setting
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".