Abstract 4513: St. Jude Survivorship Portal: A data portal for storing, analyzing, and sharing large and complex cancer survivorship datasets
Notice bibliographique
Résumé
Abstract Survivors of childhood cancer are at risk for developing various adverse health conditions as adults that are attributable to the cancer and treatments they were exposed to as children. Cancer survivorship research relies on large-scale, longitudinal studies that generate a wide range of demographic, clinical, and genetic data on cancer survivors at multiple time points. To maximize the utility of these comprehensive datasets, we must be able to store and share these datasets in a web-based environment that can be accessed by the broader survivorship research community. Furthermore, this environment should be integrated with analytical tools for performing statistical analyses on the stored data without needing to download the data and import it into third-party analytical software. To address this need, we have created the St. Jude Survivorship Portal (https://survivorship.stjude.cloud > Clinical Data Browser), a web-based data portal for exploring, sharing, and analyzing data from survivors of pediatric cancer. The portal hosts data from two large cohorts of pediatric cancer survivors: the St. Jude Lifetime Cohort Study and the Childhood Cancer Survivor Study. The data stored on the portal consists of demographic data, clinical data, including cancer diagnosis, cancer treatment, clinical outcomes, and patient-reported data, and genetic data, including whole-genome-sequencing-derived genotypes and published polygenic risk scores computed for >500 traits. This data is organized hierarchically in a data dictionary that can be easily explored by the user. Charts and plots of variables can be quickly created, customized, and stratified with other variables, all within the portal environment. Statistical analyses, including cumulative incidence analysis and regression analysis, may also be performed within the portal. In cumulative incidence analysis, users can analyze the incidence of a variety of CTCAE-graded adverse events (e.g., cardiovascular dysfunction, neurological disorders, subsequent neoplasms) in survivors and can also compare them across different survivor populations defined by other variables. In regression analysis, users have the option to perform either a linear, logistic, or cox regression analysis and may use any of the demographic, clinical, or genetic variables on the portal as outcome or explanatory variables in the analysis. In this way, users can assess any risk factor associations within a survivor cohort and generate predictive models for outcomes of interest. Lastly, we also provide the user with the option to download the data on the portal for use in any future analyses. The St. Jude Survivorship Portal provides a comprehensive, powerful, and easy-to-use interface for sharing and analyzing childhood cancer survivorship data that will serve as a valuable research tool for the broader survivorship research community. Citation Format: Gavriel Matt, Edgar Sioson, Jian Wang, Congyu Lu, Airen Zaldivar Peraza, Karishma Gangwani, Alex Acic, Jaimin Patel, Robin Paul, Colleen Reilly, Kyla Shelton, Qi Liu, Weiyu Qiu, Cindy Im, Zhaoming Wang, Carmen L. Wilson, Nickhill Bhakta, Kirsten Ness, Gregory T. Armstrong, Melissa M. Hudson, Leslie L. Robison, Jinghui Zhang, Yutaka Yasui, Xin Zhou. St. Jude Survivorship Portal: A data portal for storing, analyzing, and sharing large and complex cancer survivorship datasets. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4513.
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 | 0,008 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,004 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,127 | 0,083 |
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 ».