Dendritic Cells as Adjuvant Therapy to Decrease Mortality for Glioblastoma Patients: Meta-Analysis
Bibliographic record
Abstract
Highlight: Dendritic cells (DC) are one type of immune therapy that is being explored to improve treatment effectiveness in glioblastoma multiforme (GBM). DC was predicted to improve survival rates in GBM patients within 3 years. Effects of DC in the fifth year need to be explored to prove their. effectiveness in increasing the GBM survival rate. ABSTRACT Introduction: Glioblastoma multiforme (GBM) is a primary neoplasm of the central nervous system with a low survival rate, requiring more effective treatment to improve long-term survival. Dendritic cell (DC) therapy is expected to reduce tumor progressivity. Obective: The purpose of this meta-analysis was to analyze the administration of DC in reducing mortality in GBM patients. Methods: A systematic literature search was conducted using the PRISMA method through the Embase database, PubMed, and the Cochrane Controlled Trials Register for relevant studies between giving DC to GBM patients with conventional therapy on the number of living patients compared to controls. Article quality was assessed using the Newcastle-Ottawa Scale and statistically analyzed using RevMan 5.4. Results: Of the 14 articles, the rates of reduction in the probability of death during the first three years after initiation of therapy were 26%, 36%, and 38%, respectively [1st-y HR: 0.74 (0.57-0.95), I2: 15%, p=0.02; 2nd-y HR: 0.64 (0.51-0.81), I2: 14%, p=0.0002; 3rd-y HR: 0.62 (0.48-0.81), I2: 0%, p=0.0004]. However, there was no difference after 5 years [HR 0.81 (0.62-1.06), I2: 0%, p=0.13]. Conclusion: The DC vaccine reduces the likelihood of death in the early years of therapy but has not been proven for long-term therapy.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.057 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".