Patient-centered care in precision medicine and end-of-life care in neuro-oncology: The role of nursing in enhancing quality of life and treatment outcomes
Bibliographic record
Abstract
Central nervous system (CNS) tumors are a leading cause of cancer-related deaths in children. While advances in pediatric neuro-oncology have improved survival rates through surgery, radiation, and chemotherapy, these treatments often lead to long-term side effects—such as cognitive impairments, endocrine issues, and secondary cancers—that can significantly affect a child’s quality of life. Precision medicine offers hope by tailoring treatments based on the tumor’s molecular and genetic profile, targeting cancer cells while minimizing harm to healthy tissue. Equally important is patient-centered care (PCC), which addresses not only the clinical but also the emotional, psychosocial, and ethical needs of patients and families. Nurses play a pivotal role in integrating precision medicine and PCC, managing treatment-related symptoms, guiding shared decision-making, and supporting families—especially during end-of-life (EOL) care. Their role is critical as many patients experience cognitive and functional decline from the disease or its treatment.This article explores the expanding role of nurses in pediatric neuro-oncology, particularly in EOL care, and emphasizes the need for a holistic approach that aligns with patients’ values and well-being. Future efforts should focus on strengthening interdisciplinary collaboration, improving communication, enhancing access to psychosocial support, and addressing ethical challenges. Continued education and support for nurses are essential to deliver personalised, compassionate care.
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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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".