The relationship between cognitive function and having diabetes in patients treated with hemodialysis
Bibliographic record
Abstract
Patients undergoing maintenance hemodialysis (MHD) have a higher prevalence of cognitive impairment and inferior cognitive performance than the general population, and those with cognitive impairment are at higher risk of death than those without cognitive impairment. Having diabetes has been associated with an increased risk of cognitive decline in end-stage kidney disease patients treated with peritoneal dialysis or kidney transplant. However, these findings may not extend to the hemodialysis population. Thus, we aim to investigate the relationship between having diabetes and cognitive function in MHD patients. This was a cross-sectional study. A total of 203 patients treated with MHD from two blood purification centers were enrolled as subjects. The Chinese version of the Montreal Cognitive Assessment (MoCA) was utilized to assess cognitive function. MHD patients with diabetes had a significantly higher prevalence of global cognitive impairment and inferior performance in global cognition, visuospatial/executive function, naming, language, abstraction and orientation tasks compared with those without diabetes. According to the multiple linear analyses, having diabetes was significantly associated with lower global cognitive function, naming, and language scores, with β coefficients and 95% CIs of −1.30 [ −2.59, −0.01], −0.25 [−0.47, −0.02], and −0.32 [−0.58, −0.07], respectively (all P < 0.05). Having diabetes could not independently predict an increased risk of global cognitive impairment. In MHD patients, having diabetes is significantly associated with lower cognitive function scores. Medical staff should evaluate early and focus on the decline of cognitive function in MHD patients with diabetes, in order to achieve early diagnosis and early intervention.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".