The Role of Nurses in the Management of Adverse Events in Patients Receiving First-Line Axitinib Plus Immuno-Oncology Agents for Advanced Renal Cell Carcinoma
Bibliographic record
Abstract
OBJECTIVES: The recent approval of first-line tyrosine kinase inhibitor plus immuno-oncology agent combination therapy for the treatment of advanced renal cell carcinoma offers substantially improved response rates and survival compared with the previous standard of care. This expansion of treatment options has also led to a greater range and complexity of potential treatment-related adverse events related to overlapping toxicities. The aim of this article is to discuss the management of common treatment-emergent adverse events (AEs) associated with axitinib plus immuno-oncology therapy, highlight the specific roles of oncology nurses in managing these events, and provide AE management resources to aid oncology nurses in their care of patients with advanced renal cell carcinoma. DATA SOURCES: Author experience, journal articles, and treatment guidelines were used. CONCLUSION: The use of oncology nurses and nurse-led innovations to monitor and assess treatments can have a positive impact on the management of AEs in cancer patients by identifying those who are most at risk, providing regular assessment, appropriate patient education, and supporting the monitoring of patient safety. IMPLICATIONS FOR NURSING PRACTICE: Skilled oncology nurses should be a key part of a team that addresses the supportive care needs and management of AEs that are associated with novel cancer treatments. Early and ongoing communication between the patient and oncology nurses regarding the development of adverse events is a critical component of maximizing treatment outcomes and quality of life.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".