Incorporation of patient-reported outcomes in pediatric cancer clinical trials: design, implementation, and dissemination
Bibliographic record
Abstract
Understanding the patient experience of treatment toxicities and their impact on health-related quality of life from cancer treatments requires asking patients using patient-reported outcomes. Over the past 20 years, the National Institutes of Health has sponsored several tools-namely, Patient-Reported Outcomes Measurement Information System measures and the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events-for precisely this purpose: to ensure valid, reliable tools to collect and detect patient-reported toxicities or adverse events and their impact on health-related quality of life. These patient-reported outcomes measures have been widely incorporated in clinical trials for adults with cancer. Yet, despite considerable work developing and validating developmentally appropriate versions of these measures for pediatric and adolescent self-report, patient-reported outcomes inclusion in pediatric and adolescent and young adult clinical trials has lagged. Here, we discuss optimal strategies to integrate validated patient-reported outcomes tools and sound analytic methodologies in clinical trials for children and adolescent and young adults with cancer, highlighting lessons learned from recent successes and ongoing experiences developing and opening cross-network trials for children and adolescent and young adults through the Children's Oncology Group for patients with classic Hodgkin lymphoma, osteosarcoma, and acute lymphoblastic leukemia.
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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 | 0.808 | 0.844 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".