Altered brain structural and cognitive impairment in end-stage renal disease patients with secondary hyperparathyroidism
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment has received attention as an important problem in patients with end-stage renal disease, although end-stage renal disease patients with secondary hyperparathyroidism have not been studied. PURPOSE: To assess the pattern of brain volume changes in end-stage renal disease patients with secondary hyperparathyroidism by using voxel-based morphometry and correlating these measures with clinical markers and the Montreal Cognitive Assessment scores. MATERIAL AND METHODS: Fifty end-stage renal disease patients with no anatomical abnormalities in conventional MRI (25 patients with secondary hyperparathyroidism, 14 men, mean age 42.20 ± 7.53 years; 25 patients without secondary hyperparathyroidism, 15 men, mean age 41.96 ± 6.17 years) were selected in this study. All patients underwent laboratory tests, neuropsychological tests, and MRI. Voxel-based morphometry analysis was performed to detect regional gray matter volume differences between the two groups. The relationships between abnormal gray matter volume and clinical markers and Montreal Cognitive Assessment scores were investigated. RESULTS: < 0.05, FWE corrected). Regarding the laboratory and neuropsychological tests, we found significant correlations between volume in these brain regions and intact parathyroid hormone levels and negative correlations with the Montreal Cognitive Assessment scores. There were no significant associations between brain volume changes and other clinical data (disease duration, urea, creatinine, and uric acid levels). CONCLUSION: Our results showed significantly increased gray matter volume in end-stage renal disease patients with secondary hyperparathyroidism, which was associated with intact parathyroid hormone levels and cognitive impairment. Serum intact parathyroid hormone levels may be a risk factor for cognitive impairment in end-stage renal disease patients with secondary hyperparathyroidism.
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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 | 0.001 | 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".