Markers of mineral metabolism and vascular access complications: The Choices for Healthy Outcomes in Caring for ESRD (CHOICE) study
Bibliographic record
Abstract
INTRODUCTION: Vascular access dysfunction is a major cause of morbidity in patients with end-stage renal disease (ESRD) on chronic hemodialysis. The effects of abnormalities in mineral metabolism on vascular access are unclear. In this study, we evaluated the association of mineral metabolites, including 25-hydroxy vitamin D (25(OH)D) and fibroblast growth factor-23 (FGF-23), with vascular access complications. METHODS: We included participants from the Choices for Healthy Outcomes in Caring for ESRD (CHOICE) Study who were using an arteriovenous fistula (AVF; n = 103) or arteriovenous graft (AVG; n = 116). Serum levels of 25(OH)D, FGF-23, parathyroid hormone (PTH), calcium, phosphorus, C-reactive protein (CRP) and interleukin-6 (IL-6) were assessed from stored samples. Participants were followed for up to 1 year or until a vascular access intervention or replacement. FINDINGS: A total of 24 participants using an AVF and 43 participants using an AVG experienced access intervention. Those with 25(OH)D level in the lowest tertile (<11 ng/mL) had an increased risk of AVF intervention compared to those with higher 25(OH)D levels (adjusted relative hazard [aHR] = 3.28; 95% confidence interval [CI]: 1.31, 8.20). The highest tertile of FGF-23 (>3750 RU/mL) was associated with greater risk of AVF intervention (aHR = 2.56; 95% CI: 1.06, 6.18). Higher PTH was associated with higher risk of AVF intervention (aHR = 1.64 per SD of log(PTH); 95% CI: 1.02, 2.62). These associations were not observed in participants using an AVG. None of the other analytes were significantly associated with AVF or AVG intervention. DISCUSSION: Low levels of 25(OH)D and high levels of FGF-23 and PTH are associated with increased risk of AVF intervention. Abnormalities in mineral metabolism are risk factors for vascular access dysfunction and potential therapeutic targets to improve 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".