Inclusion of a core patient-reported outcomes battery in adolescent and young adult cancer clinical trials
Bibliographic record
Abstract
Disparities in care, treatment-related toxicity and health-related quality of life (HRQoL) for adolescents and young adults (AYAs, aged 15-39 years) with cancer are under-addressed partly because of limited collection of patient-reported outcomes (PROs) in cancer clinical trials (CCTs). The AYA years include key developmental milestones distinct from younger and older patients, and cancer interrupts attainment of critical life goals. Lack of consensus on a standardized approach to assess HRQoL and treatment-related toxicity in AYA CCTs has limited the ability to improve patient outcomes. The National Cancer Institute's Clinical Trials Network AYA PRO Task Force was assembled to reach consensus on a core set of PROs and foster its integration into AYA CCTs. Eight key considerations for selecting the core PRO AYA battery components were identified: relevance to AYAs; importance of constructs across the age continuum; prioritization of validated measures; availability of measures without licensing fees; availability in multiple languages; applicability to different cancer types and treatments; ability to measure different HRQoL domains and toxicities; and minimized burden on patients and sites. The Task Force used a modified Delphi approach to identify key components of the PRO battery. The Patient-Reported Outcomes Measurement Information System (PROMIS) and the PRO Common Terminology Criteria for Adverse Events Measurement System met all criteria and were selected to assess HRQoL and treatment toxicity, respectively. Investigators are rapidly incorporating the recommendations of the Task Force into AYA trials. Inclusion of a standardized assessment of HRQoL and treatment toxicities in AYA CCTs is a vital first step to develop interventions to improve health outcomes for AYAs diagnosed 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 | 0.654 | 0.570 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".