St. Jude Survivorship Portal: Sharing and Analyzing Large Clinical and Genomic Datasets from Pediatric Cancer Survivors
Bibliographic record
Abstract
Childhood cancer survivorship studies generate comprehensive datasets comprising demographic, diagnosis, treatment, outcome, and genomic data from survivors. To broadly share this data, we created the St. Jude Survivorship Portal (https://survivorship.stjude.cloud), the first data portal for sharing, analyzing, and visualizing pediatric cancer survivorship data. More than 1,600 phenotypic variables and 400 million genetic variants from more than 7,700 childhood cancer survivors can be explored on this free, open-access portal. Summary statistics of variables are computed on-the-fly and visualized through interactive and customizable charts. Survivor cohorts can be customized and/or divided into groups for comparative analysis. Users can also seamlessly perform cumulative incidence and regression analyses on the stored survivorship data. Using the portal, we explored the ototoxic effects of platinum-based chemotherapy, uncovered a novel association between mental health, age, and limb amputation, and discovered a novel haplotype in MAGI3 strongly associated with cardiomyopathy specifically in survivors of African ancestry. Significance: The St. Jude Survivorship Portal is the first data portal designed to share and explore clinical and genetic data from childhood cancer survivors. The portal provides both open- and controlled-access features and will fulfill a wide range of data sharing needs of the survivorship research community and beyond. See co-corresponding author Xin Zhou discuss this research article, published simultaneously at the AACR Annual Meeting 2024: https://vimeo.com/932617204/7d99fa4958.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".