Effects of high-sensitivity C-reactive protein and left ventricular hypertrophy on cognitive function in hemodialysis patients
Bibliographic record
Abstract
Objectives To examine the effects of high-sensitivity C-reactive protein (hs-CRP) and left ventricular hypertrophy (LVH) on the cognitive function of hemodialysis (HD) patients, and to explore the relationship between hs-CRP, LVH, and cognitive impairment (CI).Methods A cross-sectional study was conducted on 232 HD patients. Besides, general clinical data were gathered, and patients’ cognitive functions were assessed using the Beijing version of the Montreal Cognitive Assessment (MoCA-BJ). CI risk factors were screened using logistic regression modeling based on hs-CRP values (low risk <1 mg/L, intermediate risk 1–3 mg/L, and high risk >3 mg/L) and LVH status (normal and hypertrophic) groupings. The synergistic effect of hs-CRP and LVH on CI was also analyzed using the EpiR package.Results Among HD patients, 122 (52.59%) patients had CI. Multifactorial logistic regression analysis showed that the following factors were associated with an increased risk of CI in HD patients: age (OR = 1.048; 95% CI 1.014–1.083; p = 0.005), LVH (OR = 3.741; 95% CI 1.828–7.657; p < 0.001), and high-risk hs-CRP levels (>3 mg/L; OR = 3.238; 95% CI 1.349–7.768; p = 0.009). In addition, there was a significant synergy between hs-CRP high risk (>3 mg/L) and LVH.Conclusion Age, LVH, and high risk of hs-CRP (>3 mg/L) were independent risk factors for CI in HD patients. Moreover, HD patients with both hs-CRP high risk (>3.0 mg/L) and LVH were at higher risk of developing CI, and lowering hs-CRP levels and preventing LVH may prevent CI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".