Update: Catheter-related bloodstream infection rates in relation to clinical practice and needleless device type.
Bibliographic record
Abstract
Catheter-related bloodstream infection (CR-BSI), the third most common healthcare-associated infection (HAI) in the intensive care unit, is a significant issue for infection prevention and control professionals. CR-BSIs result in significant increases in morbidity, mortality, length of hospital stay and financial costs and therefore must be regarded as a failure in patient care. Among the factors affecting CR-BSI rates are the type of needleless access device, access device disinfection methods, compliance with infection prevention and control procedures, clinician training and ongoing education, the number of individuals accessing the device, and patient characteristics. Consistent implementation of institutional infection prevention and control protocols has demonstrated a reduction in CR-BSI incidence. Recent studies in the literature on needleless access devices indicate mechanical valve access devices appear to be associated with an increased BSI rate compared to split septum access devices; however, the reasons have not been completely elucidated. Reduction in CR-BSI rates depends on adherence to best practice in infection prevention; selection of appropriate needleless intravenous (IV) infusion systems; and routine BSI surveillance, with timely dissemination of data within the institution. This article discusses the links amongst CR-BSIs and adherence to aseptic techniques for catheter insertion, access device disinfection and maintenance, and differences in needleless access device technologies. A review of patient-related factors is beyond the scope of this article.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".