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Record W4366771615 · doi:10.3389/fphar.2023.1101063

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

2023· review· en· W4366771615 on OpenAlexaboutno aff
Dan Peng, Yongqing Cai, Geng Chen, Min Hou, Zhuoma Dongzhi, Hongjun Xie, Yao Liu

Bibliographic record

VenueFrontiers in Pharmacology · 2023
Typereview
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsnot available
Fundersnot available
KeywordsApatinibMedicineSorafenibJadad scaleInternal medicineFunnel plotPublication biasMeta-analysisPlaceboOncologyRandomized controlled trialAdverse effectHepatocellular carcinomaCancerSurgeryCochrane LibraryPathologyAlternative medicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.030
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0220.047
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.180
GPT teacher head0.394
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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