Prognostic role of neutrophil to lymphocyte ratio in lung cancers: a meta-analysis including 7,054 patients
Bibliographic record
Abstract
BACKGROUND: Neutrophil to lymphocyte ratio (NLR) has recently been reported to be a poor prognostic indicator in lung cancer. However, the prognostic value of the NLR in patients with lung cancer still remains controversial. We performed a meta-analysis to evaluate the prognostic value of NLR in patients with lung cancer. METHODS: We performed a comprehensive literature search in PubMed, Ovid, the Cochrane Library, and Web of Science databases in May 2015. Studies were assessed for quality using the Newcastle-Ottawa Scale. RESULTS: Twenty-two studies with a total of 7,054 patients were included in this meta-analysis. The meta-analysis was performed to generate combined hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS). Our analysis results indicated that high NLR predicted poorer OS (HR, 1.51; 95% confidence interval [CI], 1.33-1.71; P<0.001) and PFS (HR, 1.33; 95% CI, 1.07-1.67; P=0.012) in patients with lung cancer. High NLR was also associated with poor OS in lung cancer treated by surgical resection (HR, 1.59; 95% CI, 1.26-1.99; P<0.001) and chemotherapy (HR, 1.15; 95% CI, 1.08-1.22; P<0.001). In addition, NLR cut-off value =5 (HR, 1.57; 95% CI, 1.16-2.12; P=0.003) and NLR cut-off value <5 (HR, 1.47; 95% CI, 1.28-1.69; P<0.001). CONCLUSION: This meta-analysis result suggested that NLR should have significant predictive ability for estimating OS and PFS in patients with lung cancer and may be as a significant biomarker in the prognosis of lung cancer.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.056 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".