Effect of serum <scp>FGF</scp>‐23, <scp>MGP</scp> and fetuin‐<scp>A</scp> on calcium‐phosphate metabolism in maintenance hemodialysis patients
Bibliographic record
Abstract
This study was aimed to explore the role of serum fibroblast growth factor (FGF)-23, matrix Gla protein (MGP) and fetuin-A in the calcium-phosphate metabolism and their predicting value in coronary artery calcification in maintenance hemodialysis (MHD) patients. This study included 64 patients who receive hemodialysis in our hospital. The serum FGF-23, MGP and fetuin-A were analyzed by enzyme-linked immunosorbent assay (ELlSA). Coronary artery calcification score (CACS) was evaluated by coronary artery computed tomography scan. The 64 patients (30 males, 34 females, 60.6 ± 11.3 years of age) received an average dialysis vintage of 6.88 ± 2.94 years. We divided the CACS into three levels, and 13 (20.31%), 16 (25%), and 35 (54.69%) exhibited a CACS of 0-100, 100-400, and >400, respectively. Dialysis vintage, serum FGF-23, fetuin-A, phosphorus and high-density lipoprotein-C levels were identified as independent variables of CACS by stepwise multiple regression analysis. The area under receiver operating characteristic curve indicated that serum FGF-23 and fetuin-A were useful for identifying CAC in MHD patients. The cut-off value corresponding to the highest Youden's index was serum FGF-23 ≥ 256 pg/mL and fetuin-A ≤ 85 μg/mL, which was defined as the optimal predictors of CAC. Different combinations of serum FGF-23 and fetuin-A in parallel or in series effectively boosted the identification of CAC. The incidence of CAC is high in MHD patients. Serum FGF-23 and fetuin-A levels are closely correlated with CAC.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".