Patient-Reported Outcomes in Pediatric Patients With Cancer
Bibliographic record
Abstract
Patient-reported outcomes (PROs) are reported directly by the patients about their own health. The objective of this article was to provide an overview of PROs in pediatric cancer, to describe how PROs can be incorporated into pediatric cancer clinical trials, and to discuss how PROs can guide symptom management treatment choices in pediatric oncology. Pediatric patient self-report provides a distinct voice in describing their experience compared with family caregiver or clinician report. Thus, every effort should be made to allow children to self-report symptoms, functioning, and other quality-of-life impacts and to use that data to inform treatment decision making. In addition to its incorporation into routine clinical care, it is also important to incorporate PROs into clinical trials to understand the patient experience of treatment toxicities and their impact on quality of life. Key considerations include clearly articulated PRO aims, selection of outcomes, choice of PRO measures, and frequency of PRO assessments. Once PROs are integrated into routine clinical care, it will be important to enable evidence-based symptom management. Strategies should be based on clinical practice guidelines (CPGs). Development and adaptation of care pathways on the basis of CPGs is one approach to standardize evidence-based symptom management at individual institutions. PROs are important to pediatric patients with cancer and their families. Self-report should be emphasized wherever possible. Approaches to enable PRO reporting into routine clinical care and enable preventative and therapeutic actions for symptom management are important. These efforts will optimize quality of life for pediatric patients with 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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".