Platelet‐to‐lymphocyte ratio better predicts inflammation than neutrophil‐to‐lymphocyte ratio in end‐stage renal disease patients
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Bibliographic record
Abstract
Neutrophil-to-lymphocyte ratio (NLR) was introduced as a potential marker to determine inflammation in end-stage renal disease (ESRD) patients. Recently, platelet-to-lymphocyte ratio (PLR) and NLR were found to positively correlated with inflammatory markers including tumor necrosis factor-α (TNF-α) and interleukin (IL)-6 in cardiac and noncardiac patients. Data regarding PLR and its association with inflammation are lacking in hemodialysis (HD) and peritoneal dialysis (PD) patients. Hence, we aimed to determine the relationship between PLR, NLR, and inflammation in ESRD patients. This was a cross-sectional study involving 62 ESRD patients (29 females, 33 males; mean age, 49.6 ± 14.6 years) receiving PD or HD for ≥6 months in the Dialysis Unit of Necmettin Erbakan University. PLR, NLR, C-reactive protein, TNF-α, IL-6 levels were measured. PLR, NLR, serum high sensitive C-reactive protein, IL-6, and TNF-α levels were significantly higher in PD patients when compared with HD patients. ESRD patients with PLR ≥ 140 had significantly higher NLR, IL-6, and TNF-α levels when compared to patients with PLR < 139. In the bivariate correlation analysis, PLR was positively correlated with NLR, IL-6, and TNF-α in this population. When we compared the association of PLR and NLR with IL-6 (r = 0.371, P = 0.003 vs. r = 0.263, P = 0.04, respectively) and TNF-α (r = 0.334, P = 0.008 vs. r = 0.273, P = 0.032, respectively), PLR was found to be superior to NLR in terms of inflammation in ESRD patients. Simple calculation of PLR can predict inflammation better than NLR in ESRD patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 it