Platelet‐to‐Lymphocyte Ratio: One of the novel and valuable platelet indices in hemodialysis patients
Bibliographic record
Abstract
I would like to thank to Balta et al.1 for their constructive comments on our manuscript titled “Platelet-to-lymphocyte ratio (PLR) better predicts inflammation than neutrophil-to-lymphocyte ratio (NLR) in end-stage renal disease patients (ESRD).”2 In this study, we demonstrated that PLR might be a better indicator of inflammation when compared to NLR in ESRD patients. In ESRD patients, chronic inflammation is one of the major causes of endothelial dysfunction and vascular calcification.3 In recent years, NLR was found to be significantly correlated with inflammatory marker including hs-C-reactive protein, pentraxin-3, tumor necrosis factor-α and interleukin-6 in this population receiving renal replacement therapy4, 5 and in autosomal dominant polycystic kidney disease patients.6 Hence, to predict inflammation, NLR might be used in this population. There is also growing evidence that other variables such as PLR, red cell distribution width, platelet distribution width, platelet crit and mean platelet volume might predict inflammation. We agree with Balta et al. regarding the usage of these markers together to predict inflammation. Unfortunately, to date, there is no scoring system including these parameters to define the inflammatory status in ESRD population. Hence, NLR and PLR might be used to predict inflammation in this population accurately.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".