Proactive strategies for regorafenib in metastatic colorectal cancer: implications for optimal patient management
Bibliographic record
Abstract
Regorafenib is a broad-spectrum oral multikinase inhibitor that targets several angiogenic, oncogenic, and stromal receptor tyrosine kinases that support the tumor microenvironment. Results from the pivotal Phase III Patients with Metastatic Colorectal Cancer Treated with Regorafenib or Placebo After Failure of Standard Therapy (CORRECT) trial showed that the addition of regorafenib to best supportive care resulted in a significant improvement in median overall survival and progression-free survival compared with placebo plus best supportive care in patients with metastatic colorectal cancer (mCRC) following all available approved therapies. Thus, regorafenib is the first oral multikinase inhibitor indicated for mCRC; it currently has approval in the USA, EU, Japan, Canada, and Singapore for the treatment of mCRC patients who have been previously treated with fluoropyrimidine-, oxaliplatin-, and irinotecan-based chemotherapy, an anti-vascular endothelial growth factor therapy, and, if the tumor is KRAS wild-type, an anti-epidermal growth factor receptor therapy. In this review, we highlight regorafenib's mechanism of action, present key efficacy data from the CORRECT trial, and discuss how to proactively manage common adverse events (eg, hand-foot skin reaction, hypertension, oral mucositis, diarrhea, and fatigue) experienced by patients receiving regorafenib. Increased awareness of potential adverse events associated with regorafenib and the implementation of proactive strategies to prevent, monitor, and manage these events early in the course of treatment will be instrumental in ensuring optimal patient management and continuation of regorafenib therapy.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".