Prognostic role of vascular endothelial growth factor in hepatocellular carcinoma: systematic review and meta-analysis
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is a highly vascular tumour that expresses vascular endothelial growth factor (VEGF). Various studies have evaluated the prognostic value of VEGF levels in HCC. Its overall test performance remains unclear, however. The aim was to perform a systematic review and meta-analysis of prognostic cohort studies evaluating the use of VEGF as a predictor of survival in patients with treated HCC. Eligible studies were identified through multiple search strategies. Studies were assessed for quality using the Newcastle-Ottawa Tool. Data were collected comparing disease-free and overall survival in patients with high VEGF levels as compared to those with low levels. Studies were pooled and summary hazard ratios were calculated. A total of 16 studies were included for meta-analysis (8 for tissue and 8 for serum). Methodological analysis indicated a trend for higher study quality with serum studies as compared to tissue-based investigations. Four distinct groups were pooled for analysis: tissue overall survival (n=251), tissue disease-free survival (n=413), serum overall survival (n=579), and serum disease-free survival (n=439). High tissue VEGF levels predicted poor overall (HR=2.15, 95% CI: 1.26-3.68) and disease-free (HR=1.69, 95% CI: 1.23-2.33) survival. Similarly, high serum VEGF levels predicted poor overall (HR=2.35, 95% CI: 1.80-3.07) and disease-free (HR=2.36, 95% CI 1.76-3.16) survival. A high degree of inter-study consistency was present in three of four groups analysed. Tissue and serum VEGF levels appear to have significant predictive ability for estimating overall survival in HCC and may be useful for defining prognosis in HCC.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".