Cognitive Impairment Early After Initiating Maintenance Hemodialysis: A Cross Sectional Study
Bibliographic record
Abstract
Background: Abnormalities in cognitive function are almost universal in patients receiving hemodialysis (HD) and are associated with worse quality of life, impaired decision making, increased healthcare utilization and mortality. While cognitive impairment in the HD population is increasingly recognized, it is unclear how quickly it develops after starting HD. Methods: This was a cross-sectional study of a cohort of low dialysis vintage HD patients (<12 months). We used the validated Cambridge Brain Science (CBS) battery of web-based tests to evaluate cognition compared to age- and sex matched controls across three cognitive domains: verbal processing, reasoning and short-term memory. Results: Forty-nine HD patients were included in this study; 43 completed the full battery of tests. The average scores for HD patients were consistently below the age and sex-matched controls. Fifty-five percent of HD patients had cognitive impairment in verbal skills, 43% in reasoning and 18% in short-term memory. Conclusions: There is a high prevalence of CI evident early after starting HD, with the largest deficits seen in reasoning and verbal processing. These deficits may be attributable to the HD treatment itself. Further studies are needed to characterize the natural history of CI in this patient population and to test interventions aimed at preventing or slowing its progression.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".