Systematic review of the significance of neutrophil to lymphocyte ratio in anastomotic leak after gastrointestinal surgeries
Bibliographic record
Abstract
INTRODUCTION: The inflammatory response is thought to be a critical initiator of epigenetic alterations. The neutrophil to lymphocyte ratio (NLR), a biomarker of inflammation, is computed by dividing the number of neutrophils by the number of lymphocytes. The primary goal of this systematic review and meta-analysis was to evaluate the pre-operative NLR of gastrointestinal surgery patients who had an anastomotic leak (AL) in comparison to those who did not AL. METHODS: We performed a comprehensive search for relevant papers published before May 4, 2022, using PubMed, Scopus, and Web of Science. Standardized mean difference (SMD) with a 95% confidence interval (CI) was pooled in meta-analysis to yield a summary estimate. We utilized the random-effects model to create pooled effects since we discovered a substantial heterogeneity level. For evaluating quality, the Newcastle-Ottawa scale (NOS) was implemented. RESULTS: The research comprised 12 studies with a total of 2940 individuals who had GI operations, 353 of whom went on to develop AL. We discovered that patients who had GI surgeries and acquired AL had significantly higher NLR levels than those who did not (random-effects model: SMD = 0.75, 95% CI = 0.11-1.38, p = 0.02). Patients with AL showed significantly higher NLR levels than control group in retrospective studies (SMD = 0.93, 95% CI = 0.20-1.66, p=0.01) but not in prospective studies (SMD = - 0.11, 95% CI = - 0.65-0.43, p = 0.69), according to the subgroup analysis based on research design. Subgroup analysis based on ethnicity yielded that white patients with AL exhibited significantly higher NLR values than the control group (SMD = 1.35, 95% CI = 0.01-2.68, p = 0.04) but this result was not applied to East Asian patients (SMD = 0.14, 95% CI = -0.13-0.41, p = 0.29). CONCLUSION: Our research suggests a potential association between preoperative NLR and postoperative AL. However, it is essential to acknowledge the variability in the findings, with significantly higher NLR levels observed in retrospective studies and among white patients, but not consistently replicated in prospective studies and among East Asian patients. Further investigations with larger and more diverse cohorts are warranted to validate these findings and explore potential factors contributing to the observed discrepancies.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| 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) | 0.004 | 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".