Montreal Cognitive Assessment (MoCA) screening mild cognitive impairment in patients with chronic kidney disease (CKD) pre-dialysis
Bibliographic record
Abstract
INTRODUCTION: Individuals with chronic kidney disease (CKD) are at higher risk of developing cognitive impairment (CI), initially mild (MCI), potentially identifiable, but still poorly diagnosed and treated. The Montreal Cognitive Assessment (MoCA) has been indicated for MCI screening in CKD. OBJECTIVE: To assess MCI in patients with CKD not yet on dialysis. METHODS: Study conducted in 72 non-elderly subjects with pre-dialysis CKD. The neuropsychological assessment included: The global cognitive assessment test MoCA; the clock drawing (CD); the digit span forward (DSF) and reverse (DSR); phonemic verbal (VF) fluency (FAS) and semantics (animals); the fist-edge-palm (FEP); and the memory 10 pictures. RESULTS: The average age of the participants was 56.74 ± 7.63 years, with predominance of male sex (55.6%), mainly with ≥ 4 years of education (84.3%), with CKD cathegories 1, 2 and 3a and 3b (67.6%), hypertension (93.1%) and diabetes mellitus (52.1%). MCI (MoCA ≤ 24) was observed in 73.6% of the patients. We did not find association among MCI with demographic and clinical variables, but a tendency to association with age (p = 0.07), educational level (p= 0.06) and diabetes (0.06). The executive function tests CD, DS-reverse and FEP, individually were able to identify CI with good sensibility and negative predictive value compared to MoCA and together, showed the same capability to identify MCI when compared to MoCA. CONCLUSION: The MCI is common in non-elderly patients with CKD not yet on dialysis. Together, the CD, DSR and FEP showed similar performance in identify MCI in this population when compared to MoCA, suggesting impairment of executive functions.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".