Update on Recommendations for Cancer Screening and Surveillance in Children with Genomic Instability Disorders
Bibliographic record
Abstract
Genomic instability disorders are characterized by DNA or chromosomal instability, resulting in various clinical manifestations, including developmental anomalies, immunodeficiency, and increased risk of developing cancers beginning in childhood. Many of these genomic instability disorders also present with exquisite sensitivity to anticancer treatments such as ionizing radiation and chemotherapy, which may further increase the risk of second cancers. In July 2023, the American Association for Cancer Research held the second Childhood Cancer Predisposition Workshop, where multidisciplinary international experts discussed, reviewed, and updated recommendations for children with cancer predisposition syndromes. This article discusses childhood cancer risks and surveillance recommendations for the group of genomic instability disorders with predominantly recessive inheritance, including the DNA repair disorders ataxia telangiectasia, Nijmegen breakage syndrome, Fanconi anemia, xeroderma pigmentosum, Bloom syndrome, and Rothmund-Thomson syndrome, as well as the telomere biology disorders and mosaic variegated aneuploidy. Recognition of children with genomic instability disorders is important in order to make the proper diagnosis, enable genetic counseling, and inform cancer screening, cancer risk reduction, and choice of anticancer therapy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".