Cancer Immunotherapy: The Role of Nursing in Patient Education, Assessment, Monitoring, and Support
Bibliographic record
Abstract
The prevalence of cancer is rising both in Canada and across the world, with approximately 35 million new cases predicted by 2050. Cancer immunotherapy is a form of treatment that harnesses the body's immune system to fight cancer cells, increasing life expectancy beyond what traditional treatments offer. Immunotherapy may cause immune-related adverse events that differ from the toxicities of traditional treatments. While these events can be detrimental to health, it is critical that they are caught early. This perspective paper examines the evolving role of oncology nurses within the cancer care continuum in caring for patients receiving cancer immunotherapy, specifically immune checkpoint inhibitors. Oncology nurses provide care in many areas, specifically in educating patients on the early detection of side effects to prevent negative outcomes, assessing and monitoring patient symptoms through a variety of means, including nurse-led clinics, and providing support to patients undergoing therapy. This work helps identify gaps in the literature. Future research is required for advancing cancer immunotherapies and better detecting early signs of side effects for nurses practicing in different settings, ensuring timely care.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".