Structural network topology correlates to cognitive impairment and iPTH level in End-stage renal disease patients with peritoneal dialysis
Bibliographic record
Abstract
Abstract Objective: The burden of cognitive impairment in end-stage renal disease (ESRD) undergoing peritoneal dialysis received more attention, which associated with hospitalization, mortality and quality of life. We aimed to assess the topological alterations of brain white matter structural network in ESRD and the correlation between network metrics with Montreal Cognitive Assessment scores and clinical data. Methods: The study included 25 ESRD patients with secondary hyperparathyroidism (SHPT group), 25 patients without SHPT (Non-SHPT group) and 25 healthy controls (HC group) of comparable age and sex. Cognitive function was assessed using Montreal Cognitive Assessment. WM structural network constructed by diffusion tensor imaging and deterministic tractography method, and then used graph theoretical approaches to investigate alterations in the global and regional properties of the WM network in these participants. Results: ESRD patients showed cognitive impairment compared to HC and SHPT patients was lower cognitive scores than Non-SHPT patients. The global topological organization and local efficiency of WM network was significantly disrupted in SHPT but not in Non-SHPT patients, as compared with HC group. Moreover, lower regional efficiency was found in ESRD patients mainly distributed in the frontal and parietal cortices. In addition, association was found between iPTH, shortest path length and cognitive impairment, and iPTH level was negatively correlated with small-worldness by two indexes normalized clustering coefficient and normalized shortest path length. Conclusion: The present study indicated that brain structural connectome in ESRD patients with high iPTH level was disrupted as cognitive impairment and it has the potential connectome-based biomarkers for early detection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".