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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".