Cross-sectional assessment of mild cognitive impairment in pre-dialysis chronic kidney disease and its association with inflammation and changes seen on MRI: what the eyes cannot see
Bibliographic record
Abstract
Abstract Introduction: Mild cognitive impairment (MCI) is a prevalent and underdiagnosed condition in chronic kidney disease (CKD), that shares common pathophysiological factors such as chronic inflammation. Objective: To evaluate the association of MCI in CKD stages 1-5 using inflammatory markers and changes by magnetic resonance imaging (MRI). Patients and Methods: Cross-sectional study in adult patients with pre-dialysis CKD. MCI was assessed by the Montreal Cognitive Assessment (MoCA) and the estimated glomerular filtration rate (eGFR) by the Chronic Kidney Disease Epidemiology Collaboration equation. Sociodemographic and clinical data were collected from medical records. The cytokines IL-4, IL-6, IL-17, TNF-α and hs-CRP were determined. Brain MRI was performed in a 1.5 Tesla device, without paramagnetic contrast. A descriptive analysis followed by a comparison of abnormal versus normal MoCA scores among all studied variables. A linear regression analysis was performed using MoCA as a dependent variable, adjusted for confounding factors. Results: Of 111 invited patients, eighty completed the neuropsychological assessment and 56 underwent MRI, and were included in the study. Mean age was 56.3 ± 8.3 years and 51.8% (n = 29) had altered MoCA. When compared to the group with normal MoCA, the group with altered MoCA had higher levels of IL-6 and IL-17. There was no correlation between altered MoCA with eGFR or with MRI abnormalities. Conclusão: MCI assessed by MoCA was prevalent in patients with pre-dialysis CKD, it was associated with inflammation and showed no correlation with MRI changes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".