Prognostic value of neutrophil-lymphocyte ratio in gastroenteropancreatic neuroendocrine neoplasm: a systematic review and meta-analysis
Bibliographic record
Abstract
Purpose A high neutrophil-to-lymphocyte ratio (NLR) might be connected with an unfavorable tumor prognosis. We sought to conduct a meta-analysis of published studies exploring the prognostic value of NLR in patients with gastroenteropancreatic neuroendocrine neoplasm (GEP-NEN). Methods We have referred to the PRISMA 2020 for the Abstracts checklist and have registered our review at the International Prospective Register of Systematic Reviews (registration number CRD42020187679). The PubMed, Embase, and Web of Science databases were screened using words like ‘neutrophil to lymphocyte ratio’, ‘neuroendocrine tumors’, and others up to July 2024. In our study, we evaluated the significance of NLR on overall survival (OS), recurrence-free survival (RFS), and progression-free survival (PFS) of patients with GEP-NEN. Subgroup analysis were conducted to identify the origins of heterogeneity and examine the impact of factor grouping. Results We gathered 18 cohorts with 2,995 cases. All included studies were high quality, with Newcastle Ottawa Scale (NOS) scores ranging from 6 to 8. The pooled analysis revealed that a higher NLR related to worse OS (hazard ratio (HR): 4.59, 95% confidence interval (CI) [3.35–6.29], p < 0.00001) and poor RFS (HR: 4.05, 95% CI [2.78–5.90], p < 0.00001) in patients with GEP-NEN. Subgroup analysis of race, tumor sites, and therapy showed good predictive significance, however, NLR is not effective in predicting the overall survival time of non-operative patients. Conclusion This meta-analysis showed that a high NLR predicted poor OS, RFS, and PFS in patients with GEP-NEN and can be used as a promising predictor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".