The first step to integrating the child's voice in adverse event reporting in oncology trials: A content validation study among pediatric oncology clinicians
Bibliographic record
Abstract
PURPOSE: Children with cancer experience significant toxicities while undergoing treatment. Documentation of adverse events (AEs) in clinical trials is mandated by federal agencies. Although many AEs are subjective, the current standard is clinician reporting. Our long-term goal is to create and validate a self-report measure of subjective AEs for children aged 7 years and older that will inform AE reporting for the National Cancer Institute's Common Terminology Criteria for Adverse Events (CTCAE). This content validation study aimed to identify which of the AEs in the current CTCAE should be included in a pediatric self-report measure. METHODS: We sought expert panel review and consensus among 187 pediatric clinicians from seven Children's Oncology Group institutions to determine which of the 790 AEs are amenable to child self-report. Two survey iterations were used to identify suitable AEs, and clinician agreement estimated by the content-validity ratio (CVR) was assessed. RESULTS: Response rates for surveys 1 and 2 were 72% and 67%, respectively. After the surveys, 64 CTCAE terms met the criteria of being subjective, relevant for use in pediatric cancer trials, and amenable to self-report by a child. The most frequent reasons for removal of CTCAE terms were that they relied on laboratory or clinical measures or were not applicable to children. CONCLUSION: The 64 CTCAE terms will be translated into child-friendly terms as the basis of the child-report toxicity measure. Ultimately, systematic collection of these data will improve care by enhancing the accuracy and completeness of treatment toxicity reports for childhood 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 | 0.286 | 0.366 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".