Predicting hepatocellular carcinoma recurrence and survival
Bibliographic record
Abstract
Background: Beta blockers can inhibit tumor growth and metastases, while necroinflammation can enhance these tumor properties.Objective: To determine whether beta blockers and necroinflammatory disease predict tumor recurrence and/or overall survival following potentially curative therapeutic interventions for patients with hepatocellular carcinoma (HCC). Methods:The medical records of 36 adults with non-metastatic HCC who had undergone surgical resections and/or radiofrequency ablation (RFA) were retrospectively reviewed.In addition to post-intervention beta blocker usage and serum alanine aminotransferase levels greater than 2xULN, other variables commonly associated with recurrences such as number and size of tumors, state of differentiation and vascular invasion were included in univariate and multivariate analyses for recurrence and survival.Results: Vascular invasion (OR 29.3, 95% CI 2.6-33.6)and surgical resection (OR 0.19, 95% CI 0.04-0.90)emerged from univariate (p=0.003 and 0.03 respectively) and multivariate (p=0.005 and 0.048 respectively) regression as predictors of tumor recurrence whereas beta blocker usage (OR 0.03, 95% CI 0.04-0.9,p=0.03) and tumor recurrence (OR 6.7, 95% CI 1.6-28.1,p=0.026) correlated with overall survival.Conclusions: Neither beta blocker usage nor serum ALT levels predict HCC recurrences, but beta blocker usage is associated with improved overall survival following potentially curative therapeutic interventions for HCC in adults.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".