Prognostic value of KRAS codon 13 gene mutation for overall survival in colorectal cancer
Bibliographic record
Abstract
BACKGROUND: The clinical significance of KRAS codon 13 mutation in patients with colorectal cancer (CRC) remains controversial. A systematic review and meta-analysis is necessary for a more precise estimation of the predictive role of KRAS codon 13 mutations in CRC patients. METHODS: We performed a systematic search using the MEDLINE, EMBASE, and Cochrane library databases from January 2000 to November 2016. The prognostic value of KRAS codon 13 mutation for overall survival (OS) was investigated by measuring the hazard ratio (HR) and 95% confidence interval (CI). Data were analyzed with Review Manager Version 5.3 and the Canadian Agency for Drugs and Technologies in Health software. RESULTS: OS in CRC patients with KRAS codon 13 mutation was worse than that in CRC patients with KRAS wild-type (pooled HR = 1.37, 95% CI: 1.03-1.81, P = .03). Subgroup analysis of studies of enrolled CRC patients treated with antiepidermal growth factor receptor (EGFR) therapy showed no significant difference in OS associated with KRAS codon 13 mutation in comparison to KRAS wild-type (pooled HR = 1.57, 95% CI: 0.98-2.51, P = .06). In the indirect comparison, no statistically significant association was observed between codon 12 and 13 mutations for OS in CRC patients (pooled HR = 0.88, 95% CI: 0.65-1.20, P = .43). CONCLUSION: The current meta-analysis suggests that Codon 13 mutation of KRAS gene seems to correlate with the OS of patients with CRC, but has similar OS to those with KRAS wild-type in patients receiving anti-EGFR therapy. No difference was detected in the OS of CRC patients with codon 13 mutation versus codon 12 mutation.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.015 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".