Abstract 2633: Advancing childhood cancer research through young investigator and advocate collaboration
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,057 | 0,054 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,004 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,002 | 0,017 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».