Elevated Fibroblast Growth Factor 23 is a Risk Factor for Kidney Transplant Loss and Mortality
Bibliographic record
Abstract
An increased circulating level of fibroblast growth factor 23 (FGF23) is an independent risk factor for mortality, cardiovascular disease, and progression of chronic kidney disease (CKD), but its role in transplant allograft and patient survival is unknown. We tested the hypothesis that increased FGF23 is an independent risk factor for all-cause mortality and allograft loss in a prospective cohort of 984 stable kidney transplant recipients. At enrollment, estimated GFR (eGFR) was 51 ± 21 ml/min per 1.73 m(2) and median C-terminal FGF23 was 28 RU/ml (interquartile range, 20 to 43 RU/ml). Higher FGF23 levels independently associated with increased risk of the composite outcome of all-cause mortality and allograft loss (full model hazard ratio: 1.46 per SD increase in logFGF23, 95% confidence interval: 1.28 to 1.68, P<0.001). The results were similar for each component of the composite outcome and in all sensitivity analyses, including prespecified analyses of patients with baseline eGFR of 30 to 90 ml/min per 1.73 m(2). In contrast, other measures of phosphorus metabolism, including serum phosphate and parathyroid hormone (PTH) levels, did not consistently associate with outcomes. We conclude that a high (or elevated) FGF23 is an independent risk factor for death and allograft loss in kidney transplant recipients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".