EVD nursing management – exploring the differences in Australasia
Bibliographic record
Abstract
Abstract Background: Infection is a significant risk for neurosurgical patients undergoing external ventricular drain (EVD) insertion. Nurses are well placed in the care of the patient with an EVD and in the role of infection prevention. Aim: This article aims to explore evidence-based practices and variations in nursing management of EVDs across Australasia, explored through questionnaires at the 2024 Australasian Neuroscience Nurses Association (ANNA) conference and compared with the literature. Methods: A written questionnaire containing 10 pre-determined questions were provided to participants who signed up for an EVD session at the ANNA 2024 conference workshops. Results from this survey along with data from conference discussions and existing research though a librarian, with thanks to the university of Otago the search were analysed to highlight effective prevention strategies and risk factors for external Ventriculostomy drain Associated Infections (VAIs). Results: A final sample of 9 neurosurgical nurses from Australasia and Canada completed the questionnaire although more nurses attended the workshop (n = 25). Combined with findings from the literature, the survey data identified several modifiable risk factors for VAIs, including cerebrospinal fluid sampling frequency, catheter duration, and site care. Effective practices—such as tunnelling EVDs, using antimicrobial-impregnated catheters, and implementing evidence-based maintenance protocols—were consistently highlighted. Ongoing education and adherence to best-practice guidelines were recognised as key strategies to reduce infection risk. Conclusion: Standard guidelines do not necessarily need extensive changes but should be regularly reviewed and adjusted to improve practices. Continuous education is crucial for reducing VAIs. Collaborative efforts among neuroscience nurses can significantly enhance patient care and outcomes. Focus areas for nurses could include wound dressing, cleaning and sampling technique.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".