Mild cognitive impairment in maintenance hemodialysis patients: a cross-sectional survey and cohort study
Bibliographic record
Abstract
Introduction: Few studies focused on mild cognitive impairment (MCI) in maintenance hemodialysis (MHD) patients. This study was conducted to survey the prevalence, the potent risk factors of MCI in MHD patients, and further observe the progress of MCI in a period of 6 months. Methodology: Mini-Mental State Examination, and Montreal Cognitive Assessment were used to assess cognitive condition. MHD patients were enrolled from The First Affiliated Hospital of Nanjing Medical University, who had a stable hemodialysis history for more than 3 months. Results: Sixty-four MHD patients and 54 general subjects were finally included. The average age of both groups was more than 60 years. The prevalence of MCI in the MHD group was significantly higher than that in general population (60.9% vs 29.6%, P <0.05). Spearman correlation analysis indicated that MCI was related to age, comorbidities, education years, uric acid, serum albumin, and blood pressure. The prevalence and severity of MCI in the MHD group remained unchanged during the 6 months (prevalence: 59.5%–66.6%, MoCA scores: 22.9–22.5). Conclusion: MHD patients sustain a fairly high prevalence of MCI. Multiple risk factors influence the incidence and progression of MCI in MHD patients. More attention should be paid to this special population. Keywords: dementia, end stage renal disease, Montreal Cognitive Assessment, Mini-Mental State Examination
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".