Risk factors for cognitive impairment in chronic kidney disease: A cross-sectional study
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) is a leading public health problem, affecting more than 800 million people worldwide. CKD is frequently associated with complications, including cardiovascular disease, anemia, osteoporosis, and cognitive impairment (CI), which can range from moderate to severe and impact patients’ quality of life. This study aims to test the prevalence of CI among patients with CKD and determine associated disease severity measures and elements related to CI. Methods: This is a cross-sectional observational study done in a tertiary medical center in a developing country’s healthcare setting. A cohort of 319 patients with CKD has been recruited. The participants took the Montreal cognitive assessment (MoCA) test. Clinical variables included comorbidities, medications, and laboratory tests from patients’ electronic records. Multivariate logistic regression analysis was used to predict factors related to MoCA ratings of < 26 and ≥ 26 after adjusting for applicable covariates. Results: 41.7% of the individuals had a MoCA score of less than 26, indicating mild CI. Factors significantly associated with cognitive problems included older age, lower educational attainment, reduced estimated glomerular filtration rate, advanced stage of CKD, and use of benzodiazepines. Conclusion: The study highlights the high prevalence of CI among CKD patients and identifies several modifiable and non-modifiable risk factors. Early screening and targeted interventions should help reduce CKD patients’ mental suffering and CI.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".