Cognitive Dysfunction Screening in Peritoneal Dialysis Patients: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Mild cognitive impairment (MCI) in peritoneal dialysis (PD) patients has been described as a risk factor for worse outcomes such as peritonitis, technique failure, and mortality. In this study, we aimed to determine the prevalence of MCI in a population of PD patients and identify the possible risk factors associated with MCI. Materials and Methods: We performed an observational, cross-sectional study to evaluate cognitive function using the Montreal Cognitive Assessment (MOCA) test and the Mini Mental State Examination (MMSE) test in PD patients. Patients with diagnosis of dementia or severe neurologic impairment, active cancer, or infection were excluded. Results: We evaluated 66 patients (mean age 60 years); 53% were male. Prevalence of MCI assessed by MOCA test and MMSE test was 65% and 33%, respectively. Predictors of MCI with MOCA test were higher age ( P = 0.0001), lower education level ( P = 0.005), need of a helper ( P = 0.009), and continuous ambulatory PD modality ( P = 0.019). Higher Charlson comorbidity index ( P = 0.002), coronary artery disease ( P = 0.006), and peripheral artery disease ( P = 0.033) were also associated with MCI. Lower Kt/V ( P = 0.012) and lower levels of normalized protein catabolic rate (nPCR; P < 0.000) were related to MCI. MCI patients had more episodes of peritonitis ( P = 0.047). Multivariable analysis showed that lower education, Kt/V, and nPCR were the most relevant factors connected to MCI ( P = 0.029, P = 0.037, and P = 0.019, respectively). Conclusion: In our PD population, MCI was detected in more than half of the patients. Patients with MCI were older, had lower education level, more disease burden, and higher risk for developing peritonitis. Lower Kt/V and nPCR levels were associated with MCI.
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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 | 0.002 |
| 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 | 0.001 |
| 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".