INFERR-Iron infusion in haemodialysis study: INtravenous iron polymaltose for First Nations Australian patients with high FERRitin levels on haemodialysis—a protocol for a prospective open-label blinded endpoint randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: The effectiveness of erythropoiesis-stimulating agents, which are the main stay of managing anaemia of chronic kidney disease (CKD), is largely dependent on adequate body iron stores. The iron stores are determined by the levels of serum ferritin concentration and transferrin saturation. These two surrogate markers of iron stores are used to guide iron replacement therapy. Most Aboriginal and/or Torres Islander Australians of the Northern Territory (herein respectfully referred to as First Nations Australians) with end-stage kidney disease have ferritin levels higher than current guideline recommendations for iron therapy. There is no clear evidence to guide safe and effective treatment with iron in these patients. We aim to assess the impact of intravenous iron treatment on all-cause death and hospitalisation with a principal diagnosis of all-cause infection in First Nations patients on haemodialysis with anaemia, high ferritin levels and low transferrin saturation METHODS: In a prospective open-label blinded endpoint randomised controlled trial, a total of 576 participants on maintenance haemodialysis with high ferritin (> 700 μg/L and ≤ 2000 μg/L) and low transferrin saturation (< 40%) from all the 7 renal units across the Northern Territory of Australia will be randomised 1:1 to receive intravenous iron polymaltose 400 mg once monthly (200 mg during 2 consecutive haemodialysis sessions) (Arm A) or no IV iron treatment (standard treatment) (Arm B). Rescue therapy will be administered when the ferritin levels fall below 700 μg/L or when clinically indicated. The primary outcome will be the differences between the two study arms in the risk of hospitalisation with all-cause infection or death. An economic analysis and several secondary and tertiary outcomes analyses will also be performed. DISCUSSION: The INFERR clinical trial will address significant uncertainty on the safety and efficacy of iron therapy in First Nations Australians with CKD with hyperferritinaemia and evidence of iron deficiency. This will hopefully lead to the development of evidence-based guidelines. It will also provide the opportunity to explore the causes of hyperferritinaemia in First Nations Australians from the Northern Territory. TRIAL REGISTRATION: This trial is registered with The Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12620000705987 . Registered 29 June 2020.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".