The Prognostic Role of the Platelet-Lymphocytes Ratio in Gastric Cancer: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Systemic inflammatory parameters, such as the elevator PLR (platelet-lymphocyte ratio), the NLR (neutrophil-lymphocyte ratio) and the platelet count (PLT), have been found to be associated with the prognosis in gastric cancer; however, these results, especially those relating to the PLR, remain inconsistent. So we aimed to evaluate the prognostic role of the PLR in gastric cancer by conducting and presenting the findings of this meta-analysis. METHODS: We conducted a systematic literature search in PubMed, Embase and the Cochrane Library to evaluate the prognostic value of the PLR in gastric cancer. The quality of the included studies was evaluated using the Newcastle Ottawa Quality Assessment Scale (NOS). The hazard ratio (HR) /Odds Ratio (OR) and its 95% confidence were pooled using a random effects model. A funnel plot based on overall survival was used to evaluate the publication bias. RESULTS: It total, 8 studies comprising 4513 patients with gastric cancer met the pre-setting inclusion criteria. In comparison to the normal PLR, an elevated PLR was correlated with a higher risk of lymph node metastasis with an OR of 1.50 (95% Cl:1.24-1.82; I2 = 17%) and serosal invasion (T3 +T4) risk with an OR of 2.01 (95% Cl: 1.49-2.73; I2 = 55%), and an elevated PLR also increased the advanced stage (III +IV) risk with an OR of 1.99 (95% Cl: 1.60-2.46; I2 = 28%). An elevated PLR was not a reliable predictor for OS with an HR of 0.99 (95% CI: 0.9-1.1; I2 = 12%). CONCLUSIONS: An elevated PLR was correlated with a higher risk of lymph node metastasis, serosal invasion and advanced stage (III +IV) risk in gastric cancer; however, the PLR may not act as a negative predictor for the overall survival of gastric cancer.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.054 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".