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Abstract IA013: Increasing Diversity in Pediatric Cancer Clinical Trials: Challenges and Opportunities

2024· article· en· W4403247352 on OpenAlexaboutno aff
Paula Aristizabal

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsnot available
Fundersnot available
KeywordsClinical trialMedicineCancerDiversity (politics)Pediatric cancerIntensive care medicineOncologyInternal medicinePolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.925
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.886
GPT teacher head0.660
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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