Association of Neutrophil-to-Lymphocyte Ratio With Inflammation and Erythropoietin Resistance in Chronic Dialysis Patients
Bibliographic record
Abstract
BACKGROUND: Neutrophil-to-lymphocyte ratio (NLR) was widely studied as a prognostic marker in various medical and surgical specialties, but its significance in nephrology is not yet established. OBJECTIVE: We evaluated its accuracy as an inflammation biomarker in a dialysis population. DESIGN SETTING: Single-center retrospective study. PATIENTS: The records of all 550 patients who were treated with hemodialysis (HD) or peritoneal dialysis (PD) from September 2008 to March 2011 were included. MEASUREMENTS: NLR was calculated from the monthly complete blood count. METHODS: Association between NLR and markers of inflammation (C-reactive protein [CRP], serum albumin, and erythropoietin resistance index [ERI]) was measured using Spearman coefficient. RESULTS: < .001). Finally, high NLR was associated with a nonsignificant increased ERI, but we have not demonstrated a direct correlation. LIMITATIONS: CRP and albumin are not measured routinely and were ordered for a specific clinical reason leading to an indication bias. Also, no relationship with clinical outcome was established. CONCLUSIONS: NLR seems to be a good inflammatory biomarker in dialysis in addition to being easily available. However, controlled studies should be conducted to properly assess and validate NLR levels that would be clinically significant and relevant, as well as its prognostic significance and utility in a clinical setting.
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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 | 0.003 |
| 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 | 0.000 | 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".