Pain Management for the Child with Cancer in End-of-Life Care: APON Position Paper
Bibliographic record
Abstract
Although there have been major advances in the treatment of childhood cancer with an overall survival rate of more than 70%, cancer continues to be the leading cause of death in children resulting from disease. In 1998, 2,500 children in the United States died of cancer-related causes. Each year cancer kills more children than asthma, diabetes, cystic fibrosis, congenital anomalies, and acquired immunodeficiency syndrome combined. The Association of Pediatric Oncology Nurses (APON) is the leading professional organization for nurses caring for children and adolescents with cancer and their families. The highest standards of nursing practice are achieved through education, research, certification, advocacy, and affiliation. It is the position of APON that pain in the child dying of cancer can be effectively managed. This can be accomplished by making the prevention and alleviation of pain a primary goal, partnering with the patient and parents, and aggressively using appropriate pharmacologic and non-pharmacologic interventions. The pediatric oncology nurse has an essential role in the child's pain management at the end of life through nursing assessment, identifying expected outcomes, and performing and evaluating interventions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".