Preoperative γ-glutamyl transpeptidase to platelet ratio (GPR) is an independent prognostic factor for HBV-related hepatocellular carcinoma after curative hepatic resection
Bibliographic record
Abstract
Liver fibrosis and cirrhosis is associated with the prognosis of patients with hepatocellular carcinoma (HCC) after treatment. The γ-glutamyl transpeptidase to platelet ratio (GPR) is reported to predict significant liver fibrosis and cirrhosis. The aim of this study was to investigate the predictive value of preoperative GPR on the recurrence and survival of patients with HCC who underwent curative hepatectomy.A retrospective review of demographics, medical records, and prognosis of patients with hepatitis B virus (HBV)-related HCC was performed. Overall survival (OS) and disease-free survival (DFS) were evaluated using Kaplan-Meier method, and the log-rank test was used to analyze differences in recurrence and survival. Univariate and multivariate analyses were used for significance of prognostic factor.A total of 357 patients with HBV-related HCC were included in this analysis. The preoperative GPR was associated with recurrence and survival rates, independent of HCC progression or tumor marker levels, in a multivariate analysis. OS was higher in patients with a GPR <0.84 versus ≥084 (5-year survival rates 58.6% vs. 38.5%; P < 0.001). DFS was also worse in patients with a GPR ≥0.84 than in those with GPR <0.84 (5-year recurrence rates 42.8% vs. 22.8%; P < 0.001).GPR score of ≥0.84 represents a major risk factor for the poor prognosis for HBV-related HCC after hepatic resection, and GPR served as an independent predictive factor for HBV-related HCC OS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".