Neutrophil-to-lymphocyte ratio and survival outcomes in testicular cancer: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: The neutrophil-to-lymphocyte ratio (NLR) is a biomarker in inflammatory processes associated with multiple unfavorable outcomes in various diseases. This study aims to evaluate the association between NLR values and survival outcomes in patients diagnosed with testicular cancer.Methods: A systematic search was conducted in 6 electronic databases to retrieve studies evaluating NLR in patients with testicular cancer. The outcomes sought were overall survival (OS) and progression-free survival (PFS), and the effect measures were hazard ratio (HR) with a 95% confidence interval (CI). A random effects model was used for the meta-analysis. The risk of bias included in the studies was assessed according to the Newcastle–Ottawa Scale criteria. Egger test and Trim-and-fill method were used to test the publication bias among articles. Results: Six cohort studies (n= 1315) were evaluated. High NLR values are associated with a higher risk of OS (HR: 1.75; 95% CI 1.04 – 2.92, I2: 65%). However, no statistically significant association was found between NLR and PFS values. We found publication bias in the association between NLR and OS (Egger test < 0.1). This bias was corrected by using the trim-and-fill method (HR: 1.38, 95% CI 0.85 – 2.22). Conclusions: High NLR values are associated with worse OS; however, this result had publication bias, and the association was lost when this bias was corrected. Furthermore, no statistically significant association was found between NLR values and PFS.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".