Risk factors associated with cognitive dysfunction in patients on peritoneal dialysis
Bibliographic record
Abstract
Objective. Cognitive disorders are a global health problem, as they deteriorate patients' quality of life, increase mortality and the number of hospitalizations, burden the healthcare system, and raise treatment costs. The aim of this research is to determine the prevalence of cognitive dysfunction in patients undergoing peritoneal dialysis. Methods. A cross-sectional study was conducted in May 2024, at the Clinic of Nephrology and Clinical Immunology Clinical center of Vojvodina, University of Novi Sad. . The study included 30 patients undergoing peritoneal dialysis. The Montreal Cognitive Assessment (MoCA) was used as the instrument for assessing cognitive dysfunction. Results. Of the 30 patients, 55.2% were male. The most represented age category was ≥60 years, comprising 43.5%. Among comorbidities, hypertension was the most prevalent at 33.3%. Cognitive dysfunction was confirmed in 34% of the patients. A logistic regression model was applied to identify predictors of cognitive dysfunction, revealing that female patients and those over 60 years of age were at a higher risk for cognitive dysfunction. A positive correlation between the MMSE score and the total dialysis adequacy index Kt/V was also identified. Conclusion. Significant predictors of cognitive dysfunction in patients on peritoneal dialysis were: female gender, age category (above 60 years), glucose levels, daily and weekly doses of vitamin D, and the dialysis adequacy index Kt/V.
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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 | 0.001 |
| 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".