Prognostic role of platelet-to-lymphocyte ratio in patients with rectal cancer undergoing resection: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Inflammation plays a pivotal role in tumor growth, with the platelet-to-lymphocyte ratio (PLR) emerging as a promising serum biomarker for prognostic assessment in patients with cancer. However, its specific role in rectal cancer remains controversial. Methods: A comprehensive literature review encompassing PubMed, EMBASE, and the Cochrane Library, spanning from their inception to March 2024, was conducted. The systematic review and meta-analysis strictly adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines (PRISMA). Quality assessment was conducted using the Newcastle-Ottawa scale (NOS). This study aimed to assess the available literature on the association of PLR with both overall survival (OS) and disease-free survival (DFS) in patients with rectal cancer undergoing resection. Results: Twenty-three observational studies, encompassing 7577 patients, were included in the analysis. These comprised 20 retrospective and 3 prospective cohort studies, with NOS scores ranging from 5 to 8. A significant association was found between high PLR and worse OS (hazard ratio [HR] 1.00; 95% confidence interval [CI] 1.00-1.01; P = 0.01). Conversely, no significant association was observed between PLR and DFS (HR 1.14; 95% CI 0.98-1.32; P = 0.09). Conclusions: PLR serves as an independent clinical predictor of OS in patients with rectal cancer treated with curative surgery, but not of DFS. This easily accessible biomarker appears to be an optimal prognostic index and may aid clinicians in predicting the prognosis of rectal cancer, facilitating the development of individualized treatment strategies.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.006 | 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".