Rh-endostatin Concomitant with Chemotherapy Versus Single Agent Chemotherapy for Treating Soft Tissue and Bone Sarcomas: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: Endostar (recombinant human endostatin (rh-endostatin)), a 20-kDa proteolytic fragment of collagen XVIII, was approved for the treatment of non-small cell lung cancer (NSCLC). Recently, several studies have evaluated the efficacy of rh-endostatin combined with chemotherapy in the treatment of bone and soft tissue sarcomas. Here, we conducted a systematic review and meta-analysis to assess available evidence. Methods: Pubmed, Embase, Web of Sciences, the Cochrane Library and two Chinese literature databases (CNKI, WanFang) were systematically searched till May 20, 2018. Randomized controlled trials (RCTs) and cohort studies which compared the outcomes of rh-endostatin combined with chemotherapy versus chemotherapy alone for treating bone sarcomas or soft tissue sarcomas were included. The primary outcome was overall survival rate (OSR). Secondary outcomes included objectiveremissionrate(ORR), clinical benefit rate (CBR), disease control rate (DCR), distant metastasis rate (DMR) and adverse effects (AEs). The methodological quality of the included studies was evaluated. Data analysis was performed by Revman 5.3 software. Results: 9 studies comprising 839 patients were included. The pooled results indicated that, compared with chemotherapeutic agents alone, rh-endostatin combined group had a significant benefit in 1-year and 2-year OSR. However, there were no difference between 5-year OSR. OR, CBR and DMR were higher in rh-endostatin combined group. No significant difference was observed in the incidence of AEs. Conclusions: Rh-endostatin combined chemotherapeutic agents significantly improved clinical efficacy compared with chemotherapeutic agents alone in treating bone and soft tissue sarcomas. Moreover, combination of rh-endostatin with chemotherapy didn't increase the incidence of AEs. But more high quality RCTs with large sample size should be done in the future to confirm the conclusion.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.043 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".