Prognostic Factors for Patients With a Large Number of Hepatocellular Carcinoma Nodules
Bibliographic record
Abstract
BACKGROUND: The prognostic factors and treatment strategies for hepatocellular carcinoma (HCC) patients with a large number of tumor nodules have not been fully elucidated. Clinical factors influencing prognosis were investigated in HCC patients with 30 or more tumor nodules. METHODS: Forty-six HCC patients with 30 or more tumor nodules participated in this study. None of them had vascular invasion and extrahepatic metastasis. Kaplan-Meier curve and Cox proportional hazard model were used for analysis. RESULTS: The median survival time of our patients was no more than 15 months, suggesting that patients with 30 or more tumor nodules may be regarded as a progressive subgroup showing poorer prognosis. In multivariate analysis, presence of between 30 and 59 tumor nodules (P = 0.002), male gender (P = 0.002), lower total bilirubin (total bilirubin < 1.0 mg/dL) (P = 0.011), transarterial chemoembolization (TACE) as an initial therapy (P = 0.027) and higher prothrombin time (P = 0.049) were significant independent factors for better overall survival. Among 39 patients who underwent TACE as an initial therapy, patients who received sorafenib therapy during follow-up showed better overall survival than those who did not (P = 0.026). Efficacy of sorafenib appeared to be more evident in patients who needed repeated transarterial treatment. CONCLUSIONS: In HCC patients with 30 or more tumor nodules, TACE as an initial therapy may be correlated with better prognosis. Sorafenib administration after the prior transarterial treatment may improve antitumor efficacy.
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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) | 0.002 | 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".