Mild cognitive impairment in older adults with pre‐dialysis patients with chronic kidney disease: Prevalence and association with physical function
Bibliographic record
Abstract
AIM: Chronic kidney disease (CKD) is a risk factor for declining cognitive and physical function. However, the prevalence of mild cognitive impairment (MCI) and its relationship with physical function is not clear. Therefore, our aim was to evaluate the prevalence of MCI and the relationship between MCI and physical function among older adults with pre-dialysis CKD. METHODS: We conducted a cross-sectional study of 120 patients, aged ≥65 years (mean age, 77.3 years), with pre-dialysis CKD but without probable dementia (Mini Mental State Examination <24). MCI was evaluated using the Japanese version of the Montreal Cognitive Assessment (MoCA-J). For analysis, patients were classified into two cognitive function groups: normal (MoCA-J ≥ 26) and MCI (MoCA-J < 26). Physical, clinical, and biochemical parameters were compared between the groups. Logistic and linear regression analyses were used to evaluate the specific association between cognitive and physical function. RESULTS: Seventy-five (62.5%) patients belonged to the MCI group. Significant differences between the two groups were identified for gait speed, balance, age, and haemoglobin concentration. After adjustment for covariates, only gait speed was significantly associated with MCI (odds ratio, 0.06; 95% confidence interval, 0.009-0,411). CONCLUSION: The prevalence of MCI among older adults with pre-dialysis CKD was as high as 62.5%. The association between MCI and reduced gait speed supports the possible interaction between physical and cognitive functions and the need for early screening.
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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 | 0.000 |
| 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".