Chemotherapy plus panitumumab/cetuximab versus chemotherapy plus bevacizumab in wild-type KRAS/RAS metastatic colorectal cancer: a meta-analysis
Bibliographic record
Abstract
It remains controversial which targeted monoclonal antibodies combined with chemotherapy can provide better efficacy in wild-type KRAS/RAS metastatic colorectal cancer (mCRC) patients. Therefore, we used this meta-analysis to assess the latest evidence of clinical outcomes. We systematically searched PubMed, Web of Science, Cochrane Library and Embase databases for eligible studies published from database inception to May 2022. RevMan 5.4 was used to conduct the meta-analysis. 11 RCTs involving a total of 3575 patients were included. Meta-analysis showed that EGFR inhibitors significantly prolonged the overall survival (OS) [HR = 0.83, 95%CI (0.73, 0.94), P = 0.003] and overall response rate (ORR) [RR = 1.11, 95%CI (1.05, 1.18), P = 0.0003] compared to VEGF inhibitors in wild-type KRAS/RAS mCRC patients, but no significant difference in progression-free survival (PFS) [HR = 0.96, 95%CI (0.87, 1.07), P = 0.50]. In subgroup analysis, the survival benefit of EGFR inhibitors was limited to first-line treatment. Our study showed that EGFR inhibitors were superior to VEGF inhibitors in wild-type KRAS/RAS mCRC patients, especially in patients with first-line treatment. However, subsequent large sample, multi-center RCTs are needed to further verify our conclusions.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.065 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".