Abstract 2633: Advancing childhood cancer research through young investigator and advocate collaboration
Bibliographic record
Abstract
Abstract Background: Cancer advocates and researchers share the same goal of driving science forward to create new therapies to cure more patients. The power of combining researchers and advocates has become of increased importance due to their complementary expertise. Therefore, advocacy is a critical component of grants and has become embedded into the Stand Up 2 Cancer (SU2C) applications. The optimal way to combine these skillsets and experiences to benefit the cancer community is currently unknown. Methods: The Saint Baldrick's Foundation (SBF)-SU2C pediatric dream team is comprised of a highly collaborative network across nine institutions in the United States and Canada. Since SU2C encourages incorporating advocacy into the team structure, we have assembled a diverse team of advocates and scientists by nominating a young investigator (YI) and advocate from each site. During the yearly in person team meeting, a day is dedicated to fostering the advocacy and scientist interaction including discussions with an AACR guest speaker and ending with a night of inspiration to share our stories. Monthly, the advocates have calls that include a young investigator to present their research in lay terms, moderated by the senior investigator team leaders. In order to further bridge this interaction, we developed a questionnaire and conducted interviews. The questionnaire is focused on understanding each member's experience at the intersection between science/advocacy, comparing to previous experiences, providing advice on incorporating advocacy into science and discussing how we can build on our work. Results: Questionnaire results show that both advocates and YI's see this structure to be valuable and beneficial by improving their science communication, designing patient-friendly clinical trials and sharing experience across institutions. YI's have the opportunity to communicate their research to a non-scientific audience, learn advocate's experience which motivates them to focus on patient priorities and learn skills for career development. For most YI's, this was their first advocacy experience. Advocates learn more about the research being conducted so they can share with patients to provide hope. They can also use this knowledge to help with fundraising, publicity and lobbying. To maintain momentum between yearly in person meetings and bridge interactions between sites, we will add monthly meetings focused on topics including science communication, legislation/government, regulatory science, central IRB protocols, and fundraising. Conclusion: Through creating a YI and advocate infrastructure, we have cultivated a supportive environment for meaningful conversation that impacts the entire research team. We see this as a model for team science by combining expertise to drive innovation forward and positively impact pediatric cancer patients, and perhaps those with adult malignancies. Citation Format: Amber K. Weiner, Gavin Lindberg, Melanie Moll, Antonia Palmer, Kevin Reidy, Sharon J. Diskin, Crystal L. Mackall, John M. Maris, Patrick J. Sullivan. Advancing childhood cancer research through young investigator and advocate collaboration [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2633.
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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 | 0.057 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".