Precision oncology for children: A primer for paediatricians
Bibliographic record
Abstract
Cancer is the leading cause of disease-related death in children, adolescents, and young adults beyond the newborn period in North America. Improving survival rates for patients with hard-to-cure cancer remains a challenge. One approach that has gained particular traction is 'precision oncology', whereby next-generation sequencing is used to identify genomic or transcriptomic changes that can help clarify the diagnosis, refine prognosis, define an underlying genetic cause, or identify a unique treatment target for a patient's cancer. In this primer, we provide a brief overview of the evolution of precision paediatric oncology, its current application to clinical oncology practice, and its future potential as a foundational approach to paediatric oncology care in Canada and around the world. We also address the many challenges and limitations inherent to the implementation of precision oncology as the standard of care, including ethical and economic considerations.
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 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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.013 | 0.038 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".