Circulating Fibroblast Growth Factor-23 Level and Paraoxonase-1 Lactonase Activity in Chronic Hemodialysis Patients: Their Impact on the Incidence of Native AV Fistula Thrombosis
Bibliographic record
Abstract
PURPOSE: Thrombosis of native arteriovenous (AV) fistula is an important cause of complications in hemodialysis (HD) patients. The purpose of this study was to investigate the usefulness of measuring circulating fibroblast growth factor-23 (FGF-23) level and paraoxonase-1 (PON1) lactonase activity as potential predictors of native AV fistula thrombosis in chronic HD patients. METHODS: This study included 83 HD patients (48 with thrombosed and 35 with non-thrombosed native AV fistulas) and 38 healthy volunteers. Serum FGF-23 level was measured using the ELISA technique, while serum PON1 lactonase activity was measured spectrophotometrically using gamma-thiobutyrolactone as a substrate. RESULTS: FGF-23 was significantly increased while PON1 lactonase was markedly decreased in both thrombosed and non-thrombosed HD patients compared with controls (P < 0.001). FGF-23 was elevated whereas PON1 lactonase was decreased in HD patients with thrombosed native AV fistulas compared with HD patients with non-thrombosed native AV fistulas (P = 0.001 and 0.002, respectively). A significant negative correlation was found between FGF-23 and PON1 lactonase in HD patients with thrombosed native AV fistulas (r = -0.342, P = 0.017). CONCLUSIONS: This study shows a potential value of FGF-23 and PON1 lactonase as predictors of native AV fistula thrombosis in HD patients.
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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 | 0.000 | 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 | 0.000 |
| 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".