Anaemia management protocols in the care of haemodialysis patients: examining patient outcomes
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To determine whether the use of a nurse-driven protocol in the haemodialysis setting is as safe and effective as traditional physician-driven approaches to anaemia management. BACKGROUND: The role of haemodialysis nurses in renal anaemia management has evolved through the implementation of nurse-driven protocols, addressing the trend of exceeding haemoglobin targets and rising costs of erythropoietin-stimulating agents. DESIGN: Retrospective, non-equivalent case control group design. METHODS: The sample was from three haemodialysis units in a control group (n = 64) and three haemodialysis units in a protocol group (n = 43). The protocol group used a nurse-driven renal anaemia management protocol, while the control group used a traditional physician-driven approach to renal anaemia management. All retrospective data were obtained from a provincial renal database. Data were analysed using chi-square tests and t-tests. Patient outcomes examined were haemoglobin levels, transferrin saturation levels, erythropoietin-stimulating agents use and intravenous iron use. Cost comparisons were determined using average use of erythropoietin-stimulating agents and intravenous iron. RESULTS: Control and protocol groups reached haemoglobin target levels. In the protocol group, 75% reached transferrin saturation target levels in comparison with 25% of the control group. Use and costs for iron was higher in the control group, while use and costs for erythropoietin was higher in the protocol group. The higher usage of erythropoietin-stimulating agents was potentially related to comorbid conditions amongst the protocol group. CONCLUSIONS: A nurse-driven protocol approach to renal anaemia management was as effective as the physician-driven approach in reaching haemoglobin and transferrin saturation levels. Further examination of the use and dosing of erythropoietin-stimulating agents and intravenous iron, their impact on haemoglobin levels related to patient comorbidities and subsequent cost effectiveness of protocols is required. RELEVANCE TO CLINICAL PRACTICE: Using a nurse-driven protocol in practice supports the independent nursing role while contributing to safe patient outcomes.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".