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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".