Bibliographic record
Abstract
Background: Cognitive impairment is common in patients with CKD and may be influenced by iron deficiency and anemia through impaired oxygen delivery and altered neuronal metabolism. This study examined the associations between iron status markers [serum iron, transferrin saturation (TSAT), ferritin] and hemoglobin with cognitive function in patients with CKD stages 3–5. Methods: We conducted a cross-sectional study involving 147 patients with a mean estimated glomerular filtration rate (eGFR) of 24.8 ± 13.6 mL/min/1.73 m2. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). Mild cognitive impairment (MCI) was defined as a MoCA score ≤23 or MMSE score ≤26. Associations between iron markers and cognitive performance were analyzed using linear and logistic regression models, adjusting for age, sex, educational level, comorbidities, physical activity, estimated glomerular filtration rate, erythropoiesis-stimulating agent use, Z-drug use, and hemoglobin. Results: Patients with MCI tended to be older, had lower educational attainment, and exhibited lower serum iron, TSAT, and physical activity levels. Higher serum iron and TSAT were independently associated with better cognitive scores on both the MoCA and MMSE in linear regression analyses. Logistic regression revealed that elevated serum iron and TSAT were significantly associated with reduced odds of MCI. No consistent associations were found for ferritin or TIBC. Hemoglobin levels were not significantly associated with cognitive status after adjustment. In domain-specific analyses, attention and abstraction on the MoCA, as well as orientation and attention/calculation on the MMSE, were positively associated with serum iron and TSAT. Conclusion: Among patients with CKD, higher serum iron and TSAT levels were associated with better global cognitive performance and lower likelihood of MCI, independent of hemoglobin levels. These findings suggest that iron status may be related to cognitive function in CKD, warranting further investigation in longitudinal studies. Funding: Private Foundation Support, Government Support – Non-U.S.
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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 | 0.000 | 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) | 0.001 | 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".