Prognostic value of platelet-to-lymphocyte ratio in hepatocellular carcinoma patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The prognostic significance of the Platelet-to-Lymphocyte Ratio (PLR) in patients with hepatocellular carcinoma (HCC) undergoing treatment with immune checkpoint inhibitors (ICIs) remains uncertain. A systematic review and meta-analysis was conducted to assess the prognostic value of PLR in HCC patients receiving ICIs. METHODS: Potential eligible studies that explored the role of pretreatment PLR in HCC patients received ICIs treatment were retrieved using PubMed, Embase, and the Cochrane Library databases up to March 31, 2024. The Newcastle-Ottawa Scale was used to assess the study quality. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were utilized to investigate the correlation between PLR and both overall survival (OS) and progression-free survival (PFS). Subgroup analysis along with assessments for publication bias and sensitivity were performed to identify any sources of heterogeneity and to confirm the reliability of the pooled outcomes. RESULTS: A total of 15 studies were analyzed, with the aggregate findings showing that elevated PLR levels were associated with poorer OS (HR: 1.79, 95%CI: 1.44-2.22, P < 0.001) and PFS (HR: 1.80, 95%CI: 1.40-2.30, P < 0.001) in HCC patients treated with ICIs. Moreover, the subgroup analyses did not alter the direction of results for OS and PFS. Publication bias and sensitivity analysis revealed that there was no significant publication bias among the articles and the pooled results were robust. CONCLUSION: These results show that elevated PLR is related to worse survival in patients with HCC treated with ICIs. PLR may therefore represent an effective indicator of prognosis in HCC undergoing ICIs treatment. TRIAL REGISTRATION: This study is registered with the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY202450079).
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| 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) | 0.002 | 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".