Cognitive function and advanced kidney disease: longitudinal trends and impact on decision-making
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it