Comparison of the efficacy and safety of selective internal radiotherapy and sorafenib alone or combined for hepatocellular carcinoma: a systematic review and Bayesian network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Selective internal radiation therapy (SIRT) is a developing technique and its efficacy and modality of application in hepatocellular carcinoma (HCC) are still controversial. This network meta-analysis aims to determine whether the efficacy and safety of SIRT alone and in combination are superior to that of sorafenib. METHODS: Four databases (PubMed, Embase, Cochrane Library, and Web of Science) were searched before August 2022. Cochrane Randomized Trial Risk of Bias Assessment Tool and the Newcastle-Ottawa scale were used to assess the quality. The outcomes of interest included overall survival (OS), progression-free survival (PFS), and adverse events (AEs). RESULTS: A total of 9 eligible trials involving 1954 patients were included, and SIRT ranked first among the three treatment modalities in terms of both OS (probability, 52.3%) and PFS (probability, 68.6%). The combination of SIRT and sorafenib did not improve OS or PFS in patients with HCC. Although the combination of SIRT and sorafenib did not raise the risk of grade 3 or higher AEs, it may have introduced more AEs than either alone. CONCLUSIONS: SIRT alone was found to be superior to sorafenib and the combination of the two in improving OS or PFS in patients with non-surgical HCC, especially in patients with combined portal vein tumor thrombus. The AEs induced by SIRT were different from those of sorafenib, but the overall toxicity was manageable, the combination of the two may cause an increase in the types of AEs that occur.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.046 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".