The Prognostic Significance of Baseline Neutrophil-to-Lymphocyte Ratio in Melanoma Patients Receiving Immunotherapy
Bibliographic record
Abstract
Immunotherapy has revolutionized the treatment in metastatic melanoma, but alternative biomarkers that are economical, simple and reliable still need to be clarified. In this study, we aimed to comprehensively analyze the prognostic significance of baseline neutrophil-to-lymphocyte ratio (NLR) in melanoma patients with immunotherapy. We searched PubMed, Embase, and Cochrane Library until September 16, 2020. Hazard ratio (HR) and 95% confidence intervals (CIs) were pooled to investigate the association of baseline NLR with overall survival (OS) and progression-free survival (PFS). Sensitivity analysis, subgroup analyses, publication bias assessment, and the Duval and Tweedie trim-and-fill method were used to evaluate the stability of results. A total of 18 studies including 2054 patients were included in our analysis. Pooled data demonstrated that higher baseline NLR was associated with a poorer OS (HR=2.46, 95% CI=1.77, 3.43) and PFS (HR=2.38, 95% CI=1.95, 2.89) of melanoma patients receiving immunotherapy. Subgroup analysis according to immunotherapy type showed that the prognostic effects of baseline NLR existed in all the subtypes of immunotherapy, including anticytotoxic T lymphocyte-associated protein 4 therapy (OS HR=2.26, 95% CI=1.43, 3.59; PFS HR=2.68, 95% CI=1.79, 4.02), antiprogrammed cell death-1 therapy (OS HR=3.08, 95% CI=2.21, 4.27; PFS HR=2.01, 95% CI=1.64, 2.47), and combination therapy (OS HR=1.75, 95% CI=1.13, 2.72; PFS HR=3.13, 95% CI=1.63, 6.03). Conclusions were still consistent in subgroup analyses stratified by study year, region, study type, sample size, analysis of HR and cuttoff of baseline NLR. Altogether, baseline NLR is a promising prognostic biomarker for melanoma patients receiving immunotherapy.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".