Prognostic Role of Neutrophil to Lymphocyte Ratio in Contrast-Induced Nephropathy: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
This meta-analysis assessed the use of the neutrophil-to-lymphocyte ratio (NLR) as a means of early detection of contrast-induced nephropathy (CIN) following diagnostic or therapeutic procedures. We used Web of Science, PubMed, and Scopus to conduct a systematic search. There was no limitation regarding language or date of publication. We reported standardized mean difference (SMD) with a 95% confidence interval (CI). Due to high heterogeneity, a random-effects model was used, and the Newcastle–Ottawa scale was used for quality assessment. Thirty-one articles were included in the analysis. Patients in the CIN group had elevated levels of NLR compared with those in the non-CIN group (SMD = 0.78, 95% CI = 0.52–1.04, P < .001). Similar results were observed in either prospective (SMD = 1.03, 95% CI = 0.13–1.93, P = .02) or retrospective studies (SMD = 0.70, 95% CI = 0.45–0.96, P < .001). The pooled sensitivity of NLR was 74.02% (95% CI = 66.54%–81.02%), and the pooled specificity was 60.58% (95% CI = 53.94%–66.84%). NLR shows potential as a cost-effective biomarker for predicting CIN associated with contrast-involved treatments. This could help implement timely interventions to mitigate CIN and improve outcomes.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.049 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".