Impact of KRAS mutational status on clinical outcomes in patients receiving capecitabine based chemotherapy.
Bibliographic record
Abstract
436 Background: Mutations affecting the KRAS gene are established predictive markers of outcome with anti–epithelial growth factor receptor antibodies in metastatic colorectal cancer (mCRC). The relevance of these markers for chemotherapy has not been established. This analysis was performed to assess the predictive impact of KRAS mutation status in patients receiving chemotherapy. Methods: KRAS mutational status was available for 223 patients treated for mCRC. Predictive analysis of mutational status by type of fluoropyrmidine the 1st-line regimen contained (either capecitabine [C] based chemotherapy or infusional 5Fluorouracil [I-5Fu]) for clinical outcomes: progression-free survival (PFS), time to chemotherapy resistance (TTCR) and overall survival (OS). Results: KRAS mutations were observed in 43.5% of the patients. 165 patients received I-5Fu, 44 patients received C. KRAS mutation status (wild type [WT] v mutated [MT]) had no prognostic impact for OS (hazard ratio [HR], 0.81; CI, 0.69 to 1.1 p=0.17) for PFS (hazard ratio [HR], 0.87; CI, 0.66 to 1.14 p=0.3) and TTCR (hazard ratio [HR], 0.85; CI, 0.65to 1.12 p=0.26). C based 1st-line chemotherapy vs. I- 5FU based was predictive of PFS (hazard ratio [HR], 0.52; CI, 0.37 to 0.74 p=0.0003) and TTCR (hazard ratio [HR], 0.54; CI, 0.38 to 0.75p=0.0005) and not of OS (hazard ratio [HR], 0.74; CI, 0.52 to 1.1 p=0.1). KRAS mutational status had predictive impact in patients receiving C based 1st-line chemotherapy on OS (hazard ratio [HR], 0.47; CI, 0.23 to 0.948 p<0.0001) TTCR (hazard ratio [HR], 0.49; CI, 0.25 to 0.97 p=0.0398) and was not predictive of PFS (hazard ratio [HR], 0.78; CI, 0.4 to 1.53 p=0.47) Conclusions: KRAS gene mutation status was predicitve for OS and for TTCR in patients who received C based 1st-line chemotherapy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".