Prognostic value of platelet to lymphocyte ratio in patients with castration-resistant prostate cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Although the platelet-to-lymphocyte ratio (PLR) has been identified as a prognostic marker in various cancers, its role in castration-resistant prostate cancer (CRPC) remains uncertain. This meta-analysis examines the prognostic significance of PLR in relation to overall survival (OS) and progression-free survival (PFS) in patients with CRPC. Methods: We systematically searched PubMed, Embase, Web of Science, and the Cochrane Library up to March 11, 2025. Two reviewers independently screened studies, extracted data, and assessed quality using the Newcastle-Ottawa Scale (NOS). Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated using a random-effects model. Sensitivity and subgroup analyses explored heterogeneity and assessed result stability. All analyses were performed using Review Manager 5.4 and STATA 15.0. Results: A total of 13 studies (14 comparison groups; 2,405 patients) were included. High PLR was significantly associated with shorter OS (HR = 1.62, 95% CI: 1.30-2.03), but not with PFS (HR = 1.25, 95% CI: 0.92-1.69). Subgroup analyses confirmed the association with poor OS in prospective studies, patients aged ≥72, European populations, those on hormone therapy, and studies using a PLR cut-off ≥150. Heterogeneity mainly arose from differences in study design, treatment, and region. Sensitivity analyses and Egger's test confirmed the robustness of findings with no publication bias. Discussion: PLR is a significant predictor of OS in CRPC and may help guide clinical risk stratification. However, its role in predicting PFS is limited. Further prospective studies are needed to validate its clinical utility.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.006 | 0.008 |
| 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".