Registered Nurses’ Association of Ontario (RNAO) best practice guideline on the assessment and management of vascular access devices
Bibliographic record
Abstract
INTRODUCTION: Vascular access is the most common invasive procedure performed in health care. This fundamental procedure must be performed in a safe and effective manner. Vascular access devices (VADs) are often the source of infections and other complications, yet there is a lack of clear guidance on VADs for health providers across different settings. A Best Practice Guideline (BPG) was developed by the Registered Nurses' Association of Ontario (RNAO) to provide evidence-based recommendations on the assessment and management of VADs. METHODS: RNAO BPGs are based on systematic reviews of the literature following the GRADE approach. Experts on the topic of vascular access were selected to form a panel. Systematic reviews were conducted on six research areas: education, vascular access specialists, blood draws, daily review of peripheral VADs, visualization technologies, and pain management. A search for relevant research studies published in English limited to January 2013 was applied to eight databases. All studies were independently assessed for eligibility and risk of bias by two reviewers based on predetermined inclusion and exclusion criteria. The GRADE approach was used to determine certainty of the evidence. RESULTS: Over 65,000 articles were screened related to the six priority research questions. Of these, 876 full-text publications were examined for relevance, with 174 articles designated to inform nine recommendations in the BPG on the subject areas of: comprehensive health teaching, practical education for health providers, blood draws, daily review of peripheral VADs, visualization technologies, and pain management. In June 2021, the RNAO published the BPG on vascular access, which included the recommendations and other supporting resources. CONCLUSION: The vascular access BPG provides high quality guidance and updated recommendations, and can serve as a primary resource for health providers assessing and managing VADs.
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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 | 0.026 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".