Prevalence of Cognitive Impairment in Patients with Iron Deficiency Anemia and CKD Introduction
Bibliographic record
Abstract
Background: Cognitive impairment is highly prevalent among the population with chronic kidney disease. Even in early stages, it has been described as occurring in 10% of cases, and in more advanced stages, it can reach up to 50%.. Understanding the prevalence of cognitive impairment and the most affected domains on the MoCA Test in the population with chronic kidney disease could help better characterize this comorbidity and identify areas of opportunity to prevent further cognitive decline and its progression to dementia. Methods: Cross-sectional study of adult patients with anemia asocida a deficit de hierrro and CKD. Variables clinical, biochemical, and MoCA Test were analyzed and Pearson correlation was realized. A p-value<0.05 was considered statistically significant. Results: A total of 47 adult patients were analyzed (68% females). The average age was 56.1 ± 14.5, 55% of patients with CKD 4 and 45% CKD, mean Hb 11.12 ± 1.1, TSI 18.8 ± 6.8, MCV 87.6 ± 6.41, MCH 29.7 ± 2.6 and median Ferritin 95.65 (3.9 – 789). 36% of patients had Hb <11 mg/dL. The distribution by educational level was 45% had ≤6 years of schooling, 21% between 79 years of schooling and 34% of patients had ≥10 years of schooling. 23% had a MoCA score >26 (normal), 49% with mild impairment, 25% with moderate impairment, and only 2% with severe impairment. Conclusion: In our study, which included a Mexican population with chronic kidney disease in stages 3b and 4 and anemia associated with absolute iron deficiency, cognitive impairment was present in 77%. According to the MoCA test, patients with chronic kidney disease in stages 3b and 4 had greater impairment in the visual-executive domains. In turn, the domain with the greatest preservation was orientation
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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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 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".