Childhood cancer data initiative: expanded access to tumor molecular profiling for children, adolescents, and young adults
Bibliographic record
Abstract
The Molecular Characterization Initiative (MCI), a key effort of the National Cancer Institute's Childhood Cancer Data Initiative (CCDI), was launched in 2022 in collaboration with the Children's Oncology Group (COG) to bring comprehensive genomic and molecular profiling to children, adolescents, and young adults diagnosed with cancer. The MCI provides paired tumor and germline molecular testing, with results returned to clinicians to inform care. Deidentified data are made available to the research community through the CCDI Data Ecosystem to facilitate the discovery of new treatment strategies. This commentary outlines the MCI's development, key accomplishments to date, and its role in laying the foundation for standardized clinical diagnostics in pediatric oncology.
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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 | 0.020 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".