Proactive strategies for regorafenib in metastatic colorectal cancer: implications for optimal patient management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| 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 it