Platelet‐to‐lymphocyte ratio better predicts inflammation than neutrophil‐to‐lymphocyte ratio in end‐stage renal disease patients
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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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 | 0.000 | 0.002 |
| 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 | 0.001 | 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".