Cognitive function and advanced kidney disease: longitudinal trends and impact on decision-making
Bibliographic record
Abstract
Background: Cognitive impairment commonly affects renal patients. But little is known about the influence of dialysis modality on cognitive trends or the influence of cognitive impairment on decision-making in renal patients. This study evaluated cognitive trends amongst chronic kidney disease (CKD), haemodialysis (HD) and peritoneal dialysis (PD) patients. The relationship between cognitive impairment and decision-making capacity (DMC) was also assessed. Methods: Patients were recruited from three outpatient clinics. Cognitive function was assessed 4-monthly for up to 2 years, using the Montreal Cognitive Assessment (MoCA) tool. Cognitive trends were assessed using mixed model analysis. DMC was assessed using the Macarthur Competency Assessment tool (MacCAT-T). MacCAT-T scores were compared between patients with cognitive impairment (MoCA <26) and those without. Results: In total, 102 (41 HD, 25 PD and 36 CKD) patients were recruited into the prospective study. After multivariate analysis, the total MoCA scores declined faster in dialysis compared with CKD patients [coefficient = −0.03, 95% confidence interval (95% CI) = −0.056 to − 0.004; P = 0.025]. The MoCA executive scores declined faster in the HD compared with PD patients (coefficient = −0.12, 95% CI = −0.233 to − 0.007; P = 0.037). DMC was assessed in 10 patients. Those with cognitive impairment had lower MacCAT-T compared with those without [median (interquartile range) 19 (17.9–19.6) versus 17.4 (16.3–18.4); P = 0.049]. Conclusions: Cognition declines faster in dialysis patients compared with CKD patients and in HD patients compared with PD patients. Cognitive impairment affects DMC in patients with advanced kidney disease.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".