Pain in Children With Central Nervous System Cancer: A Review of the Literature
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To explore the current state of the science regarding pain in children with cancer, with special attention to literature related to central nervous system (CNS) tumors. This review used the Human Response to Illness (HRTI) model as an organizing framework. DATA SOURCES: PubMed, CINAHL, and Scopus data-bases. DATA SYNTHESIS: The literature review is presented with the four components of the HRTI model, including the physiologic, pathophysiologic, experiential, and behavioral perspectives of the pain response related to childhood cancer and childhood CNS cancer. The person and environmental factors that may influence a child's pain response are described. CONCLUSIONS: Children with cancer, including CNS cancer, continue to experience pain despite the improvements in knowledge related to pain. Pain assessment and management strategies must continue to evolve and improve for nursing professionals to provide a high level of care to this patient population. The HRTI model provides an appropriate framework to gain insight into the pediatric oncology nursing role in the assessment, management, and evaluation of pain from childhood cancers. IMPLICATIONS FOR NURSING: Nurses play a vital role in pain assessment and management for children with cancer. The HRTI model can be used to identify areas of clinical practice, education, and research that require further improvements in relation to pain in children with CNS cancer.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".