Abstract B046: Addressing inequities in pediatric solid tumors: Insights from Hispanics in Puerto Rico
Bibliographic record
Abstract
Abstract Introduction: Pediatric solid tumors are a diverse group of rare cancers that occur in children. Recent advancements in therapy, such as immunotherapy, targeted therapy, and precision medicine, have shown promise in improving treatment outcomes for these cancers. Moreover, health disparities in the incidence and mortality rates of pediatric solid tumors have been reported between US racial/ethnic groups, where Hispanics and African ethnicities present higher rates of incidence and metastatic disease. Despite disparities observed in pediatric solid tumors among US Hispanics, no information exists for Puerto Rican Hispanic children with cancer. Thus, this study aims to understand and describe the epidemiology of a Hispanic population of pediatric patients diagnosed with solid tumors living in Puerto Rico. Methods: Age-adjusted incidence rates (IR) and mortality rates (MR) for specific childhood cancers (ages 0-19) were estimated using data obtained from the Incidence Case File from the Puerto Rico Central Cancer Registry for the period 2009-2020. Results: During this period, 666[MS1]pediatric patients were diagnosed with cancer, with malignant bone tumors comprising 4.7% of cases and with an age-adjusted incidence rate of 6.0 cases per million. Among pediatric patients with malignant bone tumors, 34.6% presented with regional disease and 23.1% presented with distant metastatic disease at diagnosis. The mortality rate for pediatric cancers involving bone and joints was 2.4 cases per million. Conclusions: This is a first-of-its-kind analysis in a Hispanic cohort of pediatric solid tumor patients in Puerto Rico. Understanding the disparities in solid pediatric tumors within this Hispanic population is crucial for developing targeted interventions that improve diagnosis, treatment, and survival rates for these children who face other disparities, including limited access to specialized care and economic barriers, among others. Publishing this data is essential as it raises awareness, guides resource allocation, and informs policy decisions to address healthcare inequities. Future studies should explore risk factors and differential gene expression. Acknowledgement: The authors extend gratitude to the Biostatistics and Bioinformatics Core of the UPR Comprehensive Cancer Center and the PR Central Cancer Registry for collaborating in our research. Citation Format: Carolyn M. Ruiz-Perez, Rocío K. Rivera-Valentín. Addressing inequities in pediatric solid tumors: Insights from Hispanics in Puerto Rico [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 B046.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".