International neuroblastoma risk group consortium: a model of networking for rare cancers
Bibliographic record
Abstract
It is critical to share knowledge and harmonize approaches to optimize progress in rare cancers. The International Neuroblastoma Risk Group (INRG) Task Force was formed by the 4 major neuroblastoma cooperative groups in 2004 to achieve this goal. Strategies developed for neuroblastoma are an exemplar for other rare malignancies. Data from an initial cohort of 8800 patients were transferred to the INRG Data Commons, and a data-sharing model was developed. Currently, information on more than 25 000 patients is available to the research community. The INRG staging and risk classification systems have led to harmonized approaches for therapeutic groupings. INRG consensus manuscripts have led to uniform criteria for classifying biological data, evaluating the extent of disease, and defining treatment response. More than 40 INRG research studies have been performed by investigators from around the world, including analyses of rare patients, which would not otherwise be possible. The success of this approach for neuroblastoma has been leveraged to create the Pediatric Cancer Data Commons and the Data for the Common Good. Efforts to enrich the INRG Commons with additional genomic and biomarker data, extracted electronic health records, and digital medical images are ongoing. The international networking model developed by the INRG Task Force has led to new research discoveries and progress in neuroblastoma. The approach has now been applied to 16 other cancers and conditions, including rhabdomyosarcoma, germ cell tumor, Lynch syndrome, and cancer predisposition. This framework of international collaboration and data sharing serves as a model for advancing rare adult malignancies.
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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 | 0.114 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".