Comparative effectiveness and safety of monoclonal antibodies (bevacizumab, cetuximab and panitumumab) in the treatment of metastatic colorectal cancer: Systematic review and metanalysis.
Bibliographic record
Abstract
e15008 Background: Biological agents have presented variable results in the treatment of metastatic colorectal cancer. This study aim to evaluate the effectiveness and safety of the monoclonal antibodies bevacizumab, cetuximab and panitumumab associated or not to chemotherapy in this scenario. Methods: A systematic review and metanaysis was performed based on observational cohort studies published in the following databases: MEDLINE/ Pubmed, LILACS, COCHRANE Library and EMBASE, up to march 2016. The effectiveness outcomes assessed in this trial were overall survival (OS), progression free survival (PFS), pos-progression survival (PPS), response rate based on RECIST criteria and metastasectomy rate. The safety outcome assessed was adverse events. Review Manager 5.3 software was used to perform the data metanalysis and the Newcastle-Ottawa scale evaluated the methodological quality of the observational trials included. Results: 16 trials were included in this metanalysis, 11 evaluating effectiveness and 8 assessing safety. Their methodological quality were considered moderate according to the Newcastle-Ottawa Scale. Basically, this study compared bevacizumab based therapies with no bevacizumab based therapies, once there was no enough data regarding cetuximab and panitumumab. Overall, the group treated with bevacizumab based therapies did better than those in the no bevacizumab group, with OS mean difference (MD) of 4,41 (95% CI 1.75 to 7.07; p = 0.001; I² = 86%) and PFS MD of 3.19 (95% CI 0.42 to 5.96; p = 0,02 I² = 96%). The PPS MD was also statistically significant (MD = 5.90; 95% CI 2.59 to 9.21; p = 0,0005; I² = 82%). When safety was the outcome concerned, the group treated with bevacizumab had more hypertension and gastrointestinal perforation. There was no statistical difference between the groups regarding others side effects. Conclusions: This study showed a potential benefit in using bevacizumab as a part of the treatment of mCRC. Bevacizumab improved PFS, OS and PPS and these differences were statistically significant. However, the drug also increased treatment related toxicities.
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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 | 0.042 | 0.082 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.057 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".