Prognostic role of neutrophil to lymphocyte ratio and platelet to lymphocyte ratio in prostate cancer: A meta-analysis of results from multivariate analysis
Bibliographic record
Abstract
BACKGROUND: The prognostic role of neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) in patients with prostate cancer (PCa) remains inconsistent. Here we quantify the prognostic impact of these biomarkers and assess their consistency in PCa. MATERIALS AND METHODS: 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. RESULTS: A total of 7228 patients from 18 studies were included in the meta-analysis. Overall, elevated pretreatment NLR was associated with poor overall survival (OS, HR 1.58, 95% CI 1.41-1.78, P < 0.001), progression-free survival (PFS, HR 1.95, 95% CI 1.53-2.49, P < 0.001) and biochemical recurrence-free survival (BRFS, HR 1.37, 95% CI 1.07-1.75, P = 0.011). And high pretreatment PLR was correlated with more inferior PFS (HR 1.62, 95% CI 1.20-2.19, P = 0.002), OS (HR 1.70, 95% CI 1.34-2.15, P < 0.001) and cancer-specific survival (CSS, HR 2.02, 95% CI 1.24-3.29, P = 0.005). Moreover, the subgroup analyses did not alter the direction of results for OS and PFS. CONCLUSION: Based on these findings, elevated NLR and PLR was associated with poor oncologic outcomes, and they can serve as prognostic factors in PCa patients.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.062 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.002 | 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".