Abstract 3356: Working together to put kids first: Outreach strategies driving collaborative research, data sharing and cross-disease analysis to accelerate discoveries in pediatric cancer and structural birth defects
Bibliographic record
Abstract
Abstract The Gabriella Miller Kids First Pediatric Research Program launched the Kids First Pediatric Data Resource Center (DRC) in 2017 as a collaborative, pediatric research effort with the goal of understanding the genetic causes of and links between childhood cancer and structural birth defects. The DRC is charged with developing data-driven platforms that integrate large amounts of genomic and clinical data, empowering the collaborative discovery, engagement, and necessary partnerships that are crucial for progress in our biological understanding of diseases, enabling rapid translation to personalized treatments for patients and accelerating discovery of genetic causes and shared biologic pathways within and across these conditions. The DRC is comprised of 3 cores including the Data Resource Portal Core, Data Coordination Core and the Administrative & Outreach Core (AOC). The AOC brings together researchers, physicians, and patient and foundation advocates to support collaborative research and data sharing to accelerate discoveries. The AOC specific aims are to employ outreach strategies including print, web, social media, in-person presentations, conferences, videos, webinars, e-newsletters, surveys, communication strategies, and reports to support accelerated discoveries. The AOC is committed to learning from the childhood cancer and birth defect communities. By capturing, synthesizing, and prioritizing unmet needs for development of the Kids First DRC portal, website, and materials, the AOC engages with researchers, clinicians, foundations, and patient advocates in the childhood cancer and structural birth defect communities. In its first year, the AOC partnered with 32 foundations to launch the Kids First DRC Portal and support data sharing throughout the research community. Key findings during the first six months of requirements gathering revealed the following unmet needs: a) Increase understanding of the disease types, research projects, and the investigators that are a part of the Kids First community b) Highlight the need for cross-disease analyses including structural birth defects and childhood cancers and c) Promote education on the data sharing, agreements, data availability and accessibility. The AOC, gathered pertinent user requirements, conducted educational activities, and engaged prospective users of the researcher community resulting in over 200 users and 22,000 portal views since launch and will continue to use feedback from the research community to further inform the development of the Kids First DRC tools and materials to meet the goals of the program. Citation Format: Tatiana S. Patton, Robert Moulder, Erin Alexander, Donna Vito, Jonathan Waller, Colleen Gaynor, Sarah Thomas, Bailey Farrow, Joseph Yamada, Kim Cullion, Danyelle Winchester, Angela Waanders, Allison Heath, Pichai Raman, Adam Resnick, Jena Lilly. Working together to put kids first: Outreach strategies driving collaborative research, data sharing and cross-disease analysis to accelerate discoveries in pediatric cancer and structural birth defects [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3356.
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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 | 0.080 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.041 | 0.011 |
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".