Bridging therapies for patients with hepatocellular carcinoma awaiting liver transplantation: A systematic review and meta‐analysis on intention‐to‐treat outcomes
Bibliographic record
Abstract
INTRODUCTION: Locoregional therapies are commonly used as bridging strategies to decrease the drop-out of patients with hepatocellular carcinoma (HCC) awaiting liver transplantation (LT). The present paper aims to assess the outcomes of bridging therapies in patients with HCC considered for LT according to an intention-to-treat (ITT) survival analysis. MATERIAL AND METHODS: Medline and Web of Science databases were searched for reports published before May 2021. Papers assessing adult patients with HCC considered for LT and reporting ITT survival outcomes were included. Two reviewers independently identified, extracted the data, and evaluated the papers according to Newcastle-Ottawa criteria. Outcomes analyzed were: drop-out rate; time on the waiting list; 1-, 3-, and 5-year survival after LT and based on an ITT analysis. RESULTS: The search identified 3106 records; six papers (1043 patients) met the inclusion criteria. Patients with HCC, listed for LT and submitted to bridging therapies presented a longer waiting time before LT (MD 3.77, 95% CI 2.07-5.48) in comparison with the non-interventional group. However, they presented a raised post LT after 1-year (OR 2.00, 95% CI 1.18-3.41), 3-years (OR 1.47, 95% CI 1.01-2.15), and 5-years (OR 1.50, 95% CI 1.06-2.13) survival. CONCLUSION: Patients submitted to bridging procedures, despite having a longer interval on the waiting list, presented better post-LT survival outcomes. Bridging therapies for selected patients at low risk of post-procedural complications and long expected intervals on the waiting list should be encouraged. However, further clinical trials should confirm the survival benefit of bridging therapies in patients with HCC listed for LT.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".