Abstract IA013: Increasing Diversity in Pediatric Cancer Clinical Trials: Challenges and Opportunities
Bibliographic record
Abstract
Abstract Although childhood cancer is the leading cause of death by disease past infancy in the US, with 17,000 children younger than 21 years being diagnosed annually, survival has improved dramatically, and is now greater than 80% overall. Because of unequal access to services and adverse Social Determinants of Health (SDOH) affecting underserved populations, disparities in survival exist among minoritized youth with cancer. For example, Hispanic children have higher incidence of certain cancers and poorer survival rates than non-Hispanic White children. Minorities are severely under-represented in research. This means that cancer outcomes data are largely based on data from non-Hispanic White participants, and there is incomplete information to adequately assess treatment benefits for minorities. We showed that 53% of Latino parents declined research participation, compared to 20% of non-Hispanic White parents, in our institution. There are several barriers to informed consent for clinical trials. First, there are no mandates to ensure comprehension, and informed consent forms are complex, long, and difficult to understand. This leaves parents of children with cancer with an incomplete understanding of risks, procedures, randomization, alternative treatments, and the voluntary nature of clinical trial participation. Furthermore, pediatric cancer often requires urgent treatment, leaving little time for decision-making. We developed COMPRENDO (ChildhOod Malignancy Peer REsearch NavigatiOn), a peer-navigation intervention to improve research literacy and diversity in pediatric cancer clinical trials. In this intervention, trained peer-navigators, who have the lived experience of having a child diagnosed with cancer, provide in-hospital support. The goals of COMPRENDO are to improve parents’ informed consent experience and comprehension of informed consent for cancer treatment, to explain to parents terms to be discussed by the oncologist during the treatment conference, to introduce to parents concepts of clinical trials and research, and to facilitate and empower shared decision-making. In a pilot study at Rady Children’s Hospital-San Diego, this intervention showed a significant increase in comprehension of therapeutic trials, particularly in Hispanic and Spanish-speaking parents. The demographics of children with cancer enrolled in clinical trials should be comparable to the US population, and approaches to improve enrollment must also be tailored to specific settings. Structural barriers for participation of minorities should be considered during study and informed consent design and planning. Strategies to increase clinical trial enrollment of minority individuals include: Provider-level training on patient-provider communication, development of linguistically appropriate tools, and promotion of culturally aware staff. Key strategies at the patient-level include building trust; education and awareness of clinical trials; implementation of culture, language, and health literacy-concordant interventions; and initiatives to address adverse SDOH. Citation Format: Paula Aristizabal. Increasing Diversity in Pediatric Cancer Clinical Trials: Challenges and Opportunities [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr IA013.
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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 | 0.200 | 0.375 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".