Plasma fibroblast growth factor‐23 levels are independently associated with carotid artery atherosclerosis in maintenance hemodialysis patients
Bibliographic record
Abstract
Fibroblast growth factor-23 (FGF-23) has been suggested to play a role in vascular calcification in chronic kidney disease. Common carotid artery intima-media thickness (CIMT) assessment and common carotid artery (CCA) plaque identification using ultrasound are well-recognized tools for identification and monitoring of atherosclerosis. The aim of this study was to test that elevated FGF-23 levels might be associated with carotid artery atherosclerosis in maintenance hemodialysis (HD) patients. In this cross-sectional study, plasma FGF-23 concentrations were measured using a C-terminal human enzyme-linked immunosorbent assay kit. Carotid artery intima-media thickness was measured and CCA plaques were identified by B-Mode Doppler ultrasound. One hundred twenty-eight maintenance HD patients (65 women and 63 men, mean age: 55.5 ± 13 years, mean HD vintage: 52 ± 10 months, all patients are on HD thrice a week) were involved. The mean CIMT were higher with increasing tertiles of plasma FGF-23 levels (0.66 ± 0.14 vs. 0.75 ± 0.05 vs. 0.86 ± 0.20 mm, P<0.0001). Log plasma FGF-23 were higher in patients with plaques in CCA than patients free of plaques (3.0 ± 0.17 vs. 2.7 ± 0.23, P<0.0001). Significant correlation was recorded between log plasma FGF-23 and CIMT (r=0,497, P=0.0001). In multiple regression analysis, a high log FGF-23 concentration was a significant independent risk factor of an increased CIMT. Further studies are needed to clarify whether an increased plasma FGF-23 level is a marker or a potential mechanism for atherosclerosis in patients with end-stage renal disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".