The relationship between neutrophil‐to‐lymphocyte ratio and vascular calcification in end‐stage renal disease patients
Bibliographic record
Abstract
Chronic inflammation was found to be correlated with coronary (CAC) and thoracic peri-aortic calcification (TAC) in end-stage renal disease (ESRD) patients. Neutrophil-to-lymphocyte ratio (NLR) was introduced as a potential marker to determine inflammation in cardiac and noncardiac disorders. Data regarding NLR and its association with TAC and CAC are lacking. We aimed to determine the relationship between NLR and vascular calcification in ESRD patients. This was a cross-sectional study involving 56 ESRD patients (22 females, 34 males; mean age, 49.9 ± 14.2 years) receiving peritoneal dialysis or hemodialysis for ≥6 months in the Dialysis Unit of Necmettin Erbakan University. TAC and CAC scores were measured by using an electrocardiogram-gated 64-multidetector computed tomography. NLR was calculated as the ratio of the neutrophils and lymphocytes. There was a statistically significant correlation between NLR, TACS and CACS in ESRD patients (r = 0.43, P = 0.001 and r = 0.30, P = 0.02, respectively). The stepwise linear regression analysis revealed that age, as well as NLR were independent predictors of TACS. However, increased age was the only independent predictor of CACS according to linear regression analysis. Simple calculation of NLR can predict vascular calcification in ESRD patients.
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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 | 0.000 |
| 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) | 0.001 | 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".