The prognostic and clinical value of neutrophil-to-lymphocyte ratio (NLR) in ovarian cancer: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Ovarian cancer (OC) is a major gynecological malignancy with varying prognosis. The Neutrophil-toLymphocyte Ratio (NLR) has been proposed as a potential prognostic biomarker. This study aimed to evaluate the prognostic and clinical value of NLR in OC. Methods: A systematic review and meta-analysis were performed following PRISMA guidelines, including studies that evaluated the association between NLR and survival outcomes in OC patients. Search was performed in PubMed, Embase, Web of Science, and Cochrane Library databases. Quality assessment was done using Newcastle-Ottawa Scale (NOS). Heterogeneity was assessed, and pooled hazard ratios (HRs) were calculated using fixed or random-effects models as appropriate. Results: Twenty studies involving various ethnicities, ages, and sample sizes were included. A high NLR was found to be inversely correlated with overall survival (OS) (HR= 1.21, 95% CI 1.09-1.34, P<0.001) and progression-free survival (PFS) (HR=1.20, 95% CI 1.03-1.38, P<0.001). Stratified analyses showed a stronger association in Asian patients, studies with smaller sample sizes, younger patients, and higher NLR cutoff values. Conclusion: The meta-analysis suggests a significant inverse association between NLR and survival outcomes in OC patients, emphasizing NLR's potential as a simple, cost-effective prognostic biomarker. However, substantial heterogeneity and influence of confounding factors underscore the need for further investigation.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".