Prognostic value of tumor-infiltrating lymphocytes in gliomas: A Systematic Review
Bibliographic record
Abstract
Abstract Background: Glioma is the most common primary brain tumor with poor prognosis. Some studies have learned the prognostic role of tumor infiltrating lymphocytes (TILs) in gliomas. But conflict conclusions were drawn by these studies. In order to reach an agreement, we systematically performed a meta-analysis.Method: A systematic literature research was conducted on the Web of Science, EMBASE, PubMed, Cochrane Library and China National Knowledge Infrastructure. The eligible articles which met the inclusion criteria were included in our study. The clinical outcomes of included patients were defined as progression-free survival (PFS) and overall survival (OS). The basic characteristics and relevant data were extracted. Hazard ratios (HRs) with 95% confidence intervals (CIs) were pooled to evaluate the prognostic role of TILs in gliomas. Newcastle Ottawa Scale (NOS) was used to assess the quality of included studies.Results: Eight articles published from 2008 to 2019 were finally included in our study. And 25 studies were conducted in these articles. We assessed the prognostic role of TILs in gliomas by subgroup analysis according to the subtypes of TILs. The pooled HRs for OS revealed that high density of CD3+ and CD4+ TILs were related to the poor prognosis of gliomas (HR for CD3+ TILs=1.266; HR for CD4+ TILs= 2.128). The pooled HR for PFS indicated that only high density of FOXP3+ TILs were related to poor prognosis (HR=2.785; 95%CI=1.848, 4.197).Conclusion: High density of CD3+ and CD4+ TILs may be a potential candidate for predicting poor OS of gliomas while high density of FOXP3+ TILs may serve as a good biomarker for predicting poor PFS of 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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".