Quantifying cognitive dysfunction across the spectrum of end‐stage kidney disease: A systematic review and meta‐analysis
Bibliographic record
Abstract
Cognitive dysfunction is reportedly highly prevalent among chronic kidney disease (CKD) patients. A variety of screening tools and neuropsychiatric batteries are used to quantify the magnitude and nature of this dysfunction. Our objective is to summarize the neurocognitive testing used, and determine what degree cognitive dysfunction is reported in CKD patients. All study designs published in English that contained participants who were either pre-dialysis patients, haemodialysis (HD) or peritoneal dialysis (PD) patients or renal transplant recipients were considered. Reported comparative non-CKD control data was also collected. All study designs were included. The search period encompassed articles from 1980 to May 2018. This review is registered with PROSPERO (CRD42018096568). Of the 1711 articles screened, 148 articles were relevant and used in the meta-analysis. Commonly used assessments were The Mini-Mental State Examination (MMSE), The Modified Mini-Mental State Examination, the Trails Making Tests (TMT) forms A and B and components of the Wechsler Adult Intelligence Scale: Digit Span and Digit Symbol. Means for all assessments were adjusted using a random effects model to account for the differences in variance. Adjusted mean MMSE scores were significantly lower for both pre-dialysis (26.08, n = 17 073) and HD (26.31, n = 3314) patients when compared to non-CKD controls (28.21, n = 5226). PD (58.01 s, n = 859) and HD (56.04 s, n = 2344) patients also took significantly longer to complete the Trails Making Task A than non-CKD controls (37.62 s, n = 4809). Patients with CKD, especially pre-dialysis and those requiring dialysis, are likely to exhibit impairments in cognition that can be identified with specific screening neuropsychological assessments.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".