Prevalence of BRAF gene mutation in samples of primary and metastatic colorectal cancer: A meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Understanding the prevalence and biology of BRAF gene can improve the treatment methods of cancerous patients. This study aims to estimate the prevalence of BRAF gene mutation in samples of primary and metastatic colorectal cancer using meta-analysis method. METHODS: We searched PubMed, Scopus, ScienceDirect, Ovid and Google Scholar motor engine using MeSH terms of relevant keywords. During the screening phase, titles, abstracts and full texts were reviewed and risk of bias was assessed for all selected papers based on Newcastle-Ottawa Scale (NOS) checklist. The results of the primary studies were combined using meta-analysis. RESULTS: Of 95 eligible studies entered into the meta-analysis, prevalence of BRAF gene mutation had been assessed among 19,484 primary tumour samples as well as 12,256 metastatic samples. The total prevalence of BRAF gene mutation among primary tumour samples was estimated as of 10.16% (8.09-12.22) in the world, 0.41% (0-1.89) in EMRO region, 10.06% (7.54-12.59) in EURO region, 10.33% (7.24-13.43) in SEARO region and 11.33% (7.29-15.37) in WPRO region. The pooled estimates for BRAF gene mutation in metastatic samples were 6.53% (5.09-7.96), 8.07% (5.57-10.56), 5.38% (3.75-7.02) and 5.55% (1.72-9.38) for all regions, EURO, WPRO and PAHO regions respectively. CONCLUSION: Our results showed evidences of BRAF gene mutation in one-tenth of primary colorectal tumour samples in EURO, PAHO, SEARO and WPRO regions which was considerably higher than that of the EMRO region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".