Efficacy and safety of apatinib versus sorafenib/placebo in first-line treatment for intermediate and advanced primary liver cancer: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Apatinib is a novel tyrosine kinase inhibitor used in the treatment of advanced hepatocellular carcinoma (HCC). For decades, sorafenib has been a classic first-line treatment option for patients with HCC. This meta-analysis aimed to assess the efficacy and safety of apatinib versus sorafenib/placebo as first-line treatment for intermediate and advanced primary liver cancer (PLC). Methods: A literature search was performed via PubMed, Web of Science, CENTRAL, Embase, CNKI, VIP, and CBM. Data extraction from databases of other languages is not restricted. The Cochrane risk of bias tool, modified Jadad scale, Newcastle–Ottawa scale (NOS), and non-randomized studies of interventions (ROBINS-I) tool were employed to evaluate methodological qualities in original studies. Influence analysis was applied to assess the reliability of pooled results. Publication bias was evaluated using the funnel plot with Begg’s test and Egger’s test. Results: Seven studies were included in the systematic review and meta-analysis. Four randomized controlled trials (RCTs) and one clinical controlled trial (CCT) were used for comparing apatinib with placebo, and two retrospective clinical studies (RCSs) were used for comparing apatinib with sorafenib. Apatinib led to higher overall effects in objective response rate (ORR), disease control rate (DCR), and mean survival time (MST) over placebo (RR = 2.03, 95% CI = 1.46–2.81, p < 0.0001, I2 = 0%; RR = 1.17, 95% CI = 1.04–1.33, p = 0.009, I2 = 45.8%; SMD = 2.63; 95% CI = 1.47–3.78, p < 0.0001, I2 = 92.7%, respectively). Compared to sorafenib, apatinib showed no superiority in ORR and DCR but was inferior in the 6-month and 1-year survival rate (RR = 1.99, 95% CI = 0.85–4.65, p = 0.111, I2 = 68.3%; RR = 1.04, 95% CI = 0.73–1.47, p = 0.840, I2 = 0.0%; RR = 0.63, 95% CI = 0.42–0.97, p = 0.036, I2 = 0.0%; RR = 0.47, 95% CI = 0.29–0.79, p < 0.0001, I2 = 0.0%, respectively). Apatinib had similar adverse effects over placebo but possessed a greater incidence rate of proteinuria and hypertension over sorafenib. Conclusion: In the first-line setting, apatinib might be an alternative treatment approach for patients with intermediate and advanced PLC. Sorafenib alone showed a better survival rate within 1 year and a lower incidence rate in hypertension and proteinuria than apatinib monotherapy.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.047 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.003 | 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".