Serum vascular endothelial growth factor level in patients with hepatocellular carcinoma undergoing liver transplantation: experience of a single Western center.
Bibliographic record
Abstract
BACKGROUND: The strongest predictor of tumor relapse after liver transplantation for hepatocellular carcinoma (HCC) is vascular invasion, appreciated only on explant analysis. High serum level of vascular endothelial growth factor (VEGF) is associated with worse outcomes after resection or locoregional therapies but its role in liver transplantation remains undefined. OBJECTIVE: We report the first western prospective study exploring serum VEGF in HCC liver transplant patients, correlating pre-operative serum VEGF with poor prognostic histologic features during explant analysis. METHODS: Between May 2008, and June 2010, 75 HCC patients underwent liver transplantation at our institution. Serum VEGF was measured every 3 months until liver transplantation and correlated with histopathologic findings on explant. RESULTS: There was no significant correlation between pre-transplant serum VEGF levels and tumor burden (median 31.0 pg/mL vs. 42.5 pg/mL, p=0.33, for tumors within and beyond the Milan criteria, respectively). Pre-transplant VEGF levels were higher in poorly differentiated tumors compared to well to moderately differentiated tumors, but not statistically significant (median 49.0 pg/mL vs. 31.0 pg/mL, p=0.26). Pre-transplant VEGF did not correlate with vascular invasion (median 37.0 pg/mL vs. 31.0 pg/mL, p=0.35, in the presence and absence of vascular invasion, respectively). CONCLUSION: Pre-operative serum VEGF fails to predict unfavorable histologic HCC features in patients undergoing liver transplantation. Role of serum VEGF in liver transplant HCC patients remains unclear.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".