Attributes of cognitive impairment in patients on maintenance hemodialysis – A cohort study
Bibliographic record
Abstract
Abstract BACKGROUND: Chronic kidney disease patients on hemodialysis have higher cognitive impairment than the normal population due to diminishing renal function. Cognitive impairment can be assessed with the Mini-Mental State Examination or the Montreal Cognitive Assessment (MoCA). AIMS AND OBJECTIVES: The current study aims to assess mild cognitive impairment (MCI) using the MoCA examination and to document the attributes of cognitive impairment in patients on maintenance hemodialysis (MHD). MATERIALS AND METHODS: The MoCA examination was administered to all patients in Kannada using the original form SPSS 22.0, developed by SPSS Inc. in Chicago, IL, USA, and was utilized for conducting statistical analyses. RESULTS: Continuous variables were summarized by mean and standard deviation, whereas categorical data were summarized by number and percentage. Categorical variables were assessed using the Chi-square test. A value of P < 0.05 was considered as statistically significant. The mean age of the participants was 44.4 ± 15.1 years, and the mean duration of hemodialysis was 13.8 ± 14 months. About 88.6% of participants ( n = 62) showed considerable cognitive impairment and 1.4% had frank dementia. A positive association was noted between cognitive impairment and the conditions of diabetes mellitus and hypertension with a relative risk of 1.02 and 1.11, respectively. The functions of naming and orientation were perfectly correlated with the MoCA scores with r = 0.866 and r = 0.893, respectively. CONCLUSION: The study suggests that the treating physician can stress more on compliance considering the associated cognitive impairment in MHD patients. Parameters such as age, gender, and race/ethnicity influence MCI. This special population needs more attention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".