Update on Genetic Counselor Practice and Recommendations for Pediatric Cancer Predisposition Evaluation and Surveillance
Bibliographic record
Abstract
In July 2023, the American Association for Cancer Research held the second Childhood Cancer Predisposition Workshop, at which international experts in pediatric cancer predisposition met to update the previously published 2017 consensus statements on pediatric cancer predisposition syndromes. Since 2017, advances in tumor and germline genetic testing and increased understanding of cancer predisposition in patients with pediatric cancer have led to significant changes in clinical care. Here, we provide an updated genetic counseling framework for pediatric oncology professionals. The framework includes referral indications and timing, somatic and germline genetic testing options, testing for adult-onset cancer predisposition syndromes in children with and without cancer, evolving genetic counseling models to meet the increased demand for genetic testing, barriers to cancer genetic testing and surveillance in children, and psychosocial and equity considerations regarding cancer genetic testing and surveillance in children. Adaptable genetic counseling services are needed to provide support to pediatric oncology provider teams and diverse patients with pediatric cancer, cancer predisposition, and their families.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".