The neutrophil-to-lymphocyte ratio for acute allograft rejection and delayed graft function prediction in kidney transplant recipients: a meta-analysis
Bibliographic record
Abstract
Background: The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have been the focus of several observational studies investigating their roles in acute allograft rejection (AR) and delayed graft function (DGF) among kidney transplant (KT) recipients. This meta-analysis evaluated the impact of the NLR and PLR on the incidence of AR and DGF in KT recipients. Methods: We searched PubMed, MEDLINE and Science Direct from their inception through October 2023. Random effects models were used. To investigate potential sources of heterogeneity, we performed subgroup and meta-regression analyses. The Comprehensive Meta-Analysis ver. 3 software package was used. Results: Seven studies (247 KT recipients with AR or DGF and 475 controls) were analyzed. Our pooled analysis showed a significantly higher NLR in KT recipients with AR (weighted mean difference [WMD], 2.292; 95% confidence interval [CI], 1.449-3.135; P<0.001) than in controls. The preoperative NLR was insignificantly higher in patients with DGF (WMD, 0.871; 95% CI, -0.103 to 1.846; P=0.08). The PLR was insignificantly higher in KT recipients with AR than in controls (WMD, 32.125; 95% CI, -19.978 to 84.228; P=0.227). The PLR was not significantly different between KT recipients with DGF and controls. Region, publication year, sample size, donor type, biopsy type, AR type and Newcastle-Ottawa Scale score did not affect the outcomes of the meta-analysis. Meta-regression showed that publication year and donor type might be sources of heterogeneity. Conclusions: This study revealed a significantly higher NLR in patients with AR. This suggests that NLR may be utilized as a noninvasive marker for AR in KT recipients.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.064 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".