Prognostic Role of Platelet to Lymphocyte Ratio in Solid Tumors: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Inflammation influences cancer development and progression. An elevated platelet to lymphocyte ratio (PLR), a marker of inflammation, has been linked to poor prognosis in several malignancies. Here, we quantify the prognostic impact of this biomarker. METHODS: A systematic review of databases was conducted to identify publications exploring the association of blood PLR and overall survival (OS) in solid tumors. Data were pooled in a meta-analysis. Pooled HRs for OS by disease group and by PLR cutoff groups were computed and weighted using generic inverse-variance and random-effect modeling. RESULTS: Twenty studies comprising 12,754 patients were assessed. Cutoffs for PLR defining risk groups ranged from 150 to 300 and were dichotomous (12 studies; group 1) or split into three groups (<150/150-300/>300, 8 studies; group 2). Higher PLR was associated with significantly worse OS in group 1 [HR = 1.87; 95% confidence interval (CI, 1.49-2.34); P < 0.001] and with a nonsignificant association in group 2 (HR per higher category = 1.21; 95%CI, 0.97-1.50; P = 0.10). The size of effect of PLR on OS was greater for metastatic disease (HR[group 1] = 2.0; 95% CI, 1.6-2.7; HR[group 2] = 1.6; 95% CI, 1.1-2.4) than for early-stage disease (HR[group 1] = 1.5; 95% CI, 1.0-2.2; HR[group 2] = 1.0; 95% CI, 0.8-1.3). A significant association was observed for colorectal, hepatocellular, gastroesophageal, ovarian, and pancreatic carcinoma in group 1 and for colorectal cancers in group 2. CONCLUSION: A high PLR is associated with worse OS in various solid tumors. Further research of its regulation and relevance in daily practice is warranted. IMPACT: PLR is a readily available and inexpensive biomarker with independent prognostic value in solid tumors.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".