Little patients, big impacts: a narrative review of palliative and emergent radiotherapy for pediatric cancers
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The use of radiotherapy (RT) in the palliative and emergent settings for pediatric cancers is an under-utilized resource. Our objective was to provide an evidence-based review of the data to increase awareness of the benefit for this population along with providing guidance on pediatric specific treatment considerations for palliative care physicians, pediatric oncologists, and radiation oncologists. METHODS: A narrative review was performed querying PubMed, MEDLINE, ClinicalTrials.gov databases, and supplemented with review articles, survey studies, current and recent clinical trials. When limited data existed, well-designed retrospective and prospective studies in the adult setting were evaluated and expert opinion was provided from pediatric oncologists. KEY CONTENT AND FINDINGS: Pediatric specific treatment considerations include the use of anesthesia, impact of treatment on the developing child, and logistical challenges of RT. Treatment modality and dose selection are driven by histology and symptomatic site of pain, where we discuss detailed recommendations for hematologic, central nervous system, and solid tumors. For palliative RT, an underlying principle of searching for the lowest effective dose to balance response rate with minimal acute and late treatment related morbidity and logistical hardships is of paramount importance when caring for a pediatric patient. Lastly, we outline how to effectively communicate this option to patients and their caregivers. CONCLUSIONS: Palliative RT can be of valuable benefit in most settings for patients with pediatric cancer. There is an unmet need for prospective data to inform on dose-fractionation along with patient and caregiver reported outcomes.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".