Prognostic Value of Tissue Vascular Endothelial Growth Factor Expression in Bladder Cancer: a Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The prognostic role of vascular endothelial growth factor (VEGF) in bladder cancer remains controversial. This meta-analysis aimed to explore any association between overexpression and survival outcomes. METHODS: We systematically searched for studies investigating the relationships between VEGF expression and outcome of bladder cancer patients. Study quality was assessed using the Newcastle-Ottawa Scale. After careful review, survival data were extracted from eligible studies. A meta-analysis was performed to generate combined hazard ratios (HRs) for overall survival (OS), disease-free survival (DFS) and disease-specific survival (DSS). RESULTS: A total of 1,285 patients from 11 studies were included in the analysis. Our results showed that tissue VEGF overexpression in patients with bladder cancer was associated with poor prognosis in terms of OS (HR, 1.843; 95% CI, 1.231-2.759; P = 0.003), DFS (HR, 1.498; 95% CI, 1.255-1.787; P = 0.000) and DSS (HR, 1.562; 95% CI, 0.996-1.00; P = 0.052), though the difference for DSS was not statistically significant. In addition, there was no evidence of publication bias as suggested by Begg's and Egger's tests except for DFS (Begg's test, P = 0.221; Egger's test, P = 0.018). CONCLUSION: The present meta-analysis indicated elevated VEGF expression to be associated with a poor prognosis in patients with bladder 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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.053 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.002 | 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".