Treatment modalities to manage hepatocellular carcinoma patients with portal vein thrombosis: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: A number of therapeutic treatment strategies exist for patients with hepatocellular carcinoma (HCC) and portal vein thrombosis (PVT). The aim of this review is to provide a current understanding of treatment options and determine the relative effectiveness of treatment options in preventing mortality over 24 months. METHODS: A search was conducted in PubMed, EMBASE and Cochrane CENTRAL from 2007 to 2022. Articles were screened to identify those that reported on all-cause mortality among treated, non-palliative patients with HCC and PVT. Study quality was assessed using the Cochrane Risk of Bias in Non-Randomized Studies of Interventions tool (ROBINS-1). Mortality rates at prespecified timepoints between 6 and 24 months were extracted and summarized using a random-effects DerSimonian-Laird model. This review was registered a priori on PROSPERO (CRD42022290708). RESULTS: When comparing radiotherapy (RT) to sorafenib and combined transarterial chemoembolization (TACE), there was a trend that RT yields better survival at 6 months [odds ratio (OR) 0.70, 95% confidence interval (CI): 0.28-1.76]. When comparing sorafenib to Y90 and RT, sorafenib was associated with higher odds for mortality at 6 months (OR 2.20, 95% CI: 1.11-4.39). No significant differences were noticed from 12 to 24 months. CONCLUSIONS: Future strategies for HCC with PVT should look at the combination of radiation and systemic treatments either concurrently or sequentially.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".