Continuous Renal Replacement Therapy (CRRT) practices in Canadian hospitals: Where are we now?
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) is a common complication in critically ill patients. Renal replacement therapy is prescribed for as many as 70% of critically ill patients in AKI and may be provided in the form of intermittent or continuous dialysis including intermittent hemodialysis, sustained low-efficiency dialysis, and continuous renal replacement therapy (CR RT). CRRT is commonly used for unstable critically ill patients, as it involves a slow continuous process. The nursing work involved with CR RT is highly complex and the learning requirements are challenging; therefore, it is important to identify nursing practices. PURPOSE: A national survey was conducted to gain insight into CRRT nursing practices. METHODS: T he design for this study was descriptive using a survey The target population for this survey was ICU nurse educators at Canadian teaching and community hospitals. Topics in the survey included staff education, CRRT ordering and initiation practices, vascular access, filters and filter life, fluids used, anticoagulation methods, adverse events, and greatest concerns. FINDINGS: One hundred and twenty-nine surveys were sent out and 73 were returned for a response rate of 57%. Thirty-six hospitals used CRRT and of these, 73% had used CR RT for more than eight years. The findings revealed that educators identified achieving and maintaining competence as their greatest concern related to CRRT practices. CONCLUSION: The fndings of this research revealed that consistent training programs were not the norm. The complexity of caring for patients on CRRT requires consideration of when to introduce to staff the technology and care of patients on CRRT and how to sustain their competence.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".