Prognostic Significance of Preoperative Systemic Cellular Inflammatory Markers in Gliomas: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Glioma is the most common malignant brain tumor and has high lethality. This tumor generated a robust inflammatory response that results in the deterioration of the disease. However, the prognostic role of systemic cellular inflammatory indicators in gliomas remains controversial. This meta-analysis aimed to assess the prognostic significance of preoperative neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), lymphocyte/monocyte ratio (LMR), red cell distribution width (RDW), and prognostic nutritional index (PNI) in patients with gliomas. Databases of PubMed, EMBASE, Web of Science, and The Cochrane Library were systematically searched for all studies published up to January 2019. Study screening and data extraction followed established Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Newcastle-Ottawa Scale was used to assess the quality of studies. Eighteen studies containing 3,261 patients were included. The analyses showed an increased NLR or RDW was found to be an independent predictor of worse survival in patients with gliomas (hazard ratio (HR): 1.38; 95% confidence interval (CI): 1.09-1.74; P = 0.008; and HR: 1.40; 95% CI: 1.13-1.74; P = 0.002, respectively). Furthermore, a higher PNI indicates a better overall survival (OS; HR: 0.57; 95% CI: 0.42-0.77; P = 0.0002). For the evaluation of PLR and LMR, none of these variables correlated with OS (P = 0.91 and P = 0.21, respectively). Our meta-analysis indicates the NLR, RDW, and PNI rather than PLR and LMR are the independent index for predicting the OS of gliomas. Pre-operative NLR, RDW, and PNI can help to evaluate disease progression, optimize treatment, and follow-up in patients with gliomas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.006 | 0.008 |
| 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".