Factors that contribute to the cognitive impairment in elderly dialysis patients
Bibliographic record
Abstract
AIM: To evaluate the cognitive function in dialysis patients over 60 years old and identify the contributing factors. METHODS: A group of elderly dialysis patients in the Department of Nephrology, Pan'an People's Hospital between March 2015 and June 2018 were chosen as the subjects for this study. Patients were divided into two groups, those with cognitive impairment and those with normal cognitive function. Results of their Montreal Cognitive Assessment (MoCA) scores, Controlled Oral Word Association Test (COWAT), Wechsler Adult Intelligence Scale Digit Span subtest (WDMS), and Stanford Diagnostic Math Test (SDMT) were reviewed and analyzed. RESULTS: Among the 110 elderly dialysis patients, 75 patients (68.18%) showed different levels of damage to their cognitive function. Their assessment scores on MoCA (total), MoCA subtests: visuospatial/executive, naming, attention, language, delayed recall, abstraction and orientation, COWAT (total), COWAT1, COWAT2, COWAT3, WMDS-Backward, and SDMT are significantly lower than patients with normal cognitive abilities (p < 0.05). Further analysis showed that the highest percentage (72.00%) of patients had impairment with visuospatial/executive function; and, of the 75 cognitive impaired patients, 37.33% showed cognitive damage in two MoCA subtests simultaneously. Patients with and without cognitive impairment showed a significant (p < 0.05) difference on factors including age, education level, employment status, financial situation, dialysis vintage, serum albumin, and hemoglobin. CONCLUSION: Elderly patients on dialysis have a higher risk of becoming cognitive impaired. The cognitive impairment in elderly dialysis patients was significantly associated with age, dialysis vintage, and levels of serum albumin and hemoglobin.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 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".