The role of serum biomarkers in the decline of cognitive function in hemodialysis patients: a single-center retrospective study
Bibliographic record
Abstract
Background: Cognitive impairment in hemodialysis patients is higher than that in healthy individuals. However, the potential pathogenesis is not fully clear. Methods: In this retrospective study, 48 maintenance hemodialysis (MHD) patients of our center who participated in cognitive level test 4 years ago were screened by Montreal Cognitive Assessment (MoCA). We then analyzed relationships between the MoCA-derived cognitive level score, demographic, clinical, and laboratory variables. Results: The age, dialysis age, Body Mass Index (BMI), and blood calcium levels of MHD patients were significantly abnormal than 4 years ago in this study. Furthermore, we observed that the levels of fibroblast growth factor (FGF-23) in MHD patients with cognitive decline were significantly higher than those in the normal cognitive group. Receiver operating characteristic (ROC) curves demonstrated that FGF-23 was a potential biomarker for diagnosing cognitive decline in hemodialysis patients. Conclusion: We speculate that dialysis age, increased BMI, decreased blood calcium, and the abnormal level of FGF-23 may be independent risk factors for cognitive impairment in hemodialysis patients. Moreover, BMI and decreased blood calcium can be used as predictive factors of hemodialysis-related cognitive impairment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".