First- and Second-Line Treatments for Patients with Advanced Hepatocellular Carcinoma in China: A Systematic Review
Bibliographic record
Abstract
Chinese national guidelines recommend various systemic therapies for patients with advanced hepatocellular carcinoma (HCC), but optimal treatment selection remains uncertain. To summarize the evidence supporting the systemic treatment of Chinese patients with advanced HCC, we performed a systematic review using a literature search of PubMed, Embase, China National Knowledge Infrastructure, and the Chinese Scientific Journal Database between 1 January 2009 and 15 June 2021, and abstracts from ASCO 2020, ASCO GI 2021, ESMO 2020, and ESMO GI 2020. The inclusion criteria were: Chinese patients aged ≥18 years with advanced HCC; first- or second-line systemic therapy; an evaluation of the efficacy or safety outcomes; and a randomized controlled, non-randomized controlled, prospective, or retrospective design. Thirty reports were identified for the following therapies: the single-agent tyrosine kinase inhibitor (TKI; n = 10), single-agent programmed death-1 (PD-1) inhibitor (n = 4), chemotherapy (n = 5), PD-1/programmed death-ligand 1 (PD-L1) inhibitor plus TKI (n = 6), PD-1/PD-L1 inhibitor plus bevacizumab or biosimilar (n = 4), and PD-1/PD-L1 inhibitor plus chemotherapy (n = 1). The heterogeneity between the studies precluded statistical analysis and the data were summarized using tables. In the first-line setting, evidence supported the use of atezolizumab or sintilimab plus bevacizumab or a biosimilar. There remains insufficient evidence to determine the optimal approved TKI-based therapeutic option, and active controlled trials in the second-line setting were lacking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".