Prognostic significance of neutrophil–lymphocyte ratio (NLR) in patients with ovarian cancer
Bibliographic record
Abstract
The prognostic role of neutrophil to lymphocyte ratio (NLR) in patients with ovarian cancer remains inconsistent. This meta-analysis was conducted to evaluate the predictive value of this biomarker for prognoses in ovarian cancer patients.We systematically searched PubMed, Web of Science, and Embase for eligible studies embracing multivariate results. The Newcastle-Ottawa Scale were used to assess the study quality. Pooled hazard ratios (HRs), and 95% confidence intervals (CIs) were calculated.Ten studies involving 2919 patients were included in this meta-analysis. In multivariate analysis, the group with higher NLR had worse overall survival (OS) (HR = 1.34, 95% CI = 1.16-1.54) and shorter PFS (HR = 1.36, 95% CI = 1.17-1.57) than the control group. Furthermore, PLR values higher than the cut-off were associated with not only poorer OS (HR = 1.97, 95% CI = 1.61-2.40) but also more unfavorable PFS (HR = 1.79, 95% CI = 1.46-2.20). Univariate analysis also indicated the same results. Additionally, subgroup analysis showed that when the cut-off values for NLR and PLR were higher, their predictive effects became stronger.This comprehensive meta-analysis suggested that the values of inflammatory marker of NLR was associated with ovarian cancer survival. Therefore, inflammatory markers can potentially serve as prognostic biomarkers.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.019 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".