Multicenter randomized controlled trial of percutaneous cryoablation versus radiofrequency ablation in hepatocellular carcinoma
Bibliographic record
Abstract
UNLABELLED: Radiofrequency ablation (RFA) is considered a curative treatment option for hepatocellular carcinoma (HCC). Growing data have demonstrated that cryoablation represents a safe and effective alternative therapy for HCC, but no randomized controlled trial (RCT) has been reported to compare cryoablation with RFA in HCC treatment. The present study was a multicenter RCT aimed to compare the outcomes of percutaneous cryoablation with RFA for the treatment of HCC. In all, 360 patients with Child-Pugh class A or B cirrhosis and one or two HCC lesions ≤ 4 cm, treatment-naïve, without metastasis were randomly assigned to cryoablation (n = 180) or RFA (n = 180). The primary endpoints were local tumor progression at 3 years after treatment and safety. Local tumor progression rates at 1, 2, and 3 years were 3%, 7%, and 7% for cryoablation and 9%, 11%, and 11% for RFA, respectively (P = 0.043). For lesions >3 cm in diameter, the local tumor progression rate was significantly lower in the cryoablation group versus the RFA group (7.7% versus 18.2%, P = 0.041). The 1-, 3-, and 5-year overall survival rates were 97%, 67%, and 40% for cryoablation and 97%, 66%, and 38% for RFA, respectively (P = 0.747). The 1-, 3-, and 5-year tumor-free survival rates were 89%, 54%, and 35% in the cryoablation group and 84%, 50%, and 34% in the RFA group, respectively (P = 0.628). Multivariate analyses demonstrated that Child-Pugh class B and distant intrahepatic recurrence were significant negative predictors for overall survival. Major complications occurred in seven patients (3.9%) following cryoablation and in six patients (3.3%) following RFA (P = 0.776). CONCLUSION: Cryoablation resulted in a significantly lower local tumor progression than RFA, although both cryoablation and RFA were equally safe and effective, with similar 5-year survival rates.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".