Neutrophil to Lymphocyte Ratio and Spontaneous Bacterial Peritonitis among Cirrhotic Patients: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background. The goal of this systematic review and meta-analysis was analyzing published studies on the role of neutrophil to lymphocyte ratio (NLR) in infection and spatially spontaneous bacterial peritonitis (SBP) among cirrhotic patients. Methods. PubMed, Web of Science, and Scopus were searched until May 24, 2022. The Newcastle–Ottawa scale was used for quality assessment. Results. Of 14 studies included in our study, six studies were on infection with 2786 hospitalized cirrhotic patients, of whom 934 developed an infection. Other studies were on SBP with 1573 cirrhotic patients with ascites, of whom 557 developed SBP. The pooled results showed that there was no difference in NLR levels between hospitalized cirrhotic patients who developed infection compared to those who did not (random-effects model: SMD = 0.63, 95% CI = −0.01–1.27, p = 0.054 ). However, cirrhotic patients with ascites who developed SBP had elevated levels of NLR compared to those who did not (random-effects model: SMD = 1.05, 95% CI = 0.52–1.57, p < 0.001 ). This difference remained significant in prospective studies (SMD = 0.94, 95% CI = 0.51–1.38, p < 0.001 ) but not in retrospective studies (SMD = 1.37, 95% CI = −0.56–3.29, p = 0.165 ), in the subgroup analysis according to the study design. The pooled sensitivity of NLR was 92.07% (95% CI = 74.85%–97.84%) and the pooled specificity was 72.58% (95% CI = 57.72%–83.69%). The pooled positive likelihood ratio, negative likelihood ratio, DOR of NLR were 3.35(95%CI = 2.06–5.46), 0.10 (95%CI = 0.03–0.38), and 30.78 (95%CI = 7.01–135.04), respectively. Conclusion. Our results support NLR to be a valid biomarker that can be readily integrated into clinical settings to help in the prevention and prediction of SBP among cirrhotic patients.
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 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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".