A Comparison of Chemotherapy Used with and without Apatinib for Patients with Ovarian Carcinoma Who Progressed after Standard Regimens: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: This meta-analysis was conducted to compare the therapeutic efficacy and clinical safety of the combination therapy of apatinib plus chemotherapy with that of chemotherapy alone in patients with refractory or recurrent ovarian carcinoma (OC). METHODS: Relevant randomized controlled trials (RCT) or case-control studies (CCS) were identified by searching Chinese and English databases up to October 31, 2020. The risk of methodological bias tool and Newcastle-Ottawa scale (NOS) were used to assess trial quality. Pooled odds ratios (OR) and 95% confidence intervals (CI) were calculated to evaluate the therapeutic effects and adverse drug reactions. Subgroup analyses of study type, study sample size, dosage of apatinib, and chemotherapy regimen between treatment group and control group were performed. Publication bias was assessed by funnel plot symmetry, Begg-Mazumdar test, and Egger test. The robustness of our results was presented by removing the trial one by one. RESULTS: Fifteen eligible studies covering 1,020 patients were included in this review and meta-analysis. Among these studies, 8 were RCTs, and 7 were CCSs. Compared with chemotherapy alone, apatinib plus chemotherapy significantly increased objective response rate (OR = 3.55; 95% CI 2.31 to 5.47), disease control rate (OR = 3.04; 95% CI 2.12 to 4.36), and overall survival (OR = 5.03; 95% CI 3.16 to 6.90). CONCLUSIONS: The combination treatment of apatinib plus chemotherapy provides better clinical benefits for OC patients when compared to chemotherapy alone and should be recommended for suitable patients with OC after the failure of standard regimens. However, further investigation into future large-scale prospective randomized research is still needed.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.056 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".