A review of ferric citrate clinical studies, and the rationale and design of the Ferric Citrate and Chronic Kidney Disease in Children (FIT4KiD) trial
Bibliographic record
Abstract
Pediatric chronic kidney disease (CKD) is characterized by many co-morbidities, including impaired growth and development, CKD-mineral and bone disorder, anemia, dysregulated iron metabolism, and cardiovascular disease. In pediatric CKD cohorts, higher circulating concentrations of fibroblast growth factor 23 (FGF23) are associated with some of these adverse clinical outcomes, including CKD progression and left ventricular hypertrophy. It is hypothesized that lowering FGF23 levels will reduce the risk of these events and improve clinical outcomes. Reducing FGF23 levels in CKD may be accomplished by targeting two key stimuli of FGF23 production-dietary phosphate absorption and iron deficiency. Ferric citrate is approved for use as an enteral phosphate binder and iron replacement product in adults with CKD. Clinical trials in adult CKD cohorts have also demonstrated that ferric citrate decreases circulating FGF23 concentrations. This review outlines the possible deleterious effects of excess FGF23 in CKD, summarizes data from the adult CKD clinical trials of ferric citrate, and presents the Ferric Citrate and Chronic Kidney Disease in Children (FIT4KiD) study, a randomized, placebo-controlled trial to evaluate the effects of ferric citrate on FGF23 in pediatric patients with CKD stages 3-4 (ClinicalTrials.gov Identifier NCT04741646).
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".