Incidence and risk factors of cognitive dysfunction in hemodialysis patients: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: The study aims to explore the incidence and risk factors of cognitive dysfunction in hemodialysis patients. METHODS: PubMed, Embase, Cochrane Library, and Web of Science databases were searched for clinical studies on the association between hemodialysis and cognitive dysfunction from the database's inception to 1 December 2022. Two researchers independently completed data extraction and risk of bias assessments for the included studies. All statistical analyses were performed using STATA15.0 software. RESULTS: Ten studies were included in this meta-analysis, with a total of 5535 hemodialysis patients, that is, 2033 patients with cognitive dysfunction and 3502 patients with normal cognitive function. The Newcastle-Ottawa Scale scores of the included studies were greater than 5. Meta-analysis results suggested that the incidence of cognitive dysfunction in hemodialysis patients was (effect size = 51%, 95% confidence interval [CI] [0.33, 0.69]), and hemodialysis patients with cognitive dysfunction were often older than those with normal cognition (standard mean difference [SMD] = 0.49, 95% CI [0.31, 0.68]). Female gender was a risk factor for cognitive dysfunction in hemodialysis patients (relative risk [RR] = 1.21, 95% CI [1.04, 1.41]); diabetes (RR = 1.33, 95% CI [1.04, 1.71]) and stroke (RR = 1.66, 95% CI [1.08, 2.55]) increased the incidence of cognitive dysfunction in hemodialysis patients. CONCLUSIONS: The most important risk factors for cognitive dysfunction associated with hemodialysis might be female gender, old age, diabetes, and stroke. Close attention should be paid to such patients for early prevention.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".