Enhanced cognitive outcomes with triple-mode dialysis in kidney failure: role of protein-bound toxin clearance
Bibliographic record
Abstract
This study aimed to investigate the effects of different dialysis modalities on cognitive function in patients with end stage renal disease and to explore their association with the clearance of protein bound uremic toxins. A total of 135 patients undergoing maintenance hemodialysis were enrolled and randomly assigned to one of three treatment groups hemodialysis HD hemodialysis combined with hemodiafiltration HD + HDF and hemodialysis combined with hemodiafiltration plus hemoperfusion HD + HDF + HP. Cognitive function was evaluated using the Mini Mental State Examination MMSE and the Montreal Cognitive Assessment MoCA which assess multiple domains including memory attention executive function and orientation. Plasma levels of homocysteine Hcy and indoxyl sulfate IS were measured before and after dialysis sessions. Statistical analysis included comparisons among groups Spearman correlation multiple linear and logistic regression and receiver operating characteristic ROC curve analysis. Cognitive scores improved progressively across the three groups with the greatest improvement observed in the HD + HDF + HP group. Toxin levels decreased accordingly and were lowest in the HD + HDF + HP group. Multivariate analysis identified Hcy and IS as independent risk factors for cognitive impairment. ROC analysis demonstrated that both toxins had strong predictive value for cognitive decline. The results indicate that combined HDF and HP enhances removal of protein bound toxins and is associated with better preservation of cognitive function. These findings suggest that triple modality dialysis may provide a promising strategy to reduce cognitive decline and improve neurological outcomes in patients undergoing maintenance hemodialysis.
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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 | 0.001 |
| 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".