Update on Cancer Predisposition Syndromes and Surveillance Guidelines for Childhood Brain Tumors
Bibliographic record
Abstract
Tumors of the central nervous system (CNS) comprise the second most common group of neoplasms in childhood. The incidence of germline predisposition among children with brain tumors continues to grow as our knowledge on disease etiology increases. Some children with brain tumors may present with nonmalignant phenotypic features of specific syndromes (e.g., nevoid basal cell carcinoma syndrome, neurofibromatosis type 1 and type 2, DICER1 syndrome, and constitutional mismatch-repair deficiency), while others may present with a strong family history of cancer (e.g., Li-Fraumeni syndrome) or with a rare tumor commonly found in the context of germline predisposition (e.g., rhabdoid tumor predisposition syndrome). Approximately 50% of patients with a brain tumor may be the first in a family identified to have a predisposition. The past decade has witnessed a rapid expansion in our molecular understanding of CNS tumors. A significant proportion of CNS tumors are now well characterized and known to harbor specific genetic changes that can be found in the germline. Additional novel predisposition syndromes are also being described. Identification of these germline syndromes in individual patients has not only enabled cascade testing of family members and early tumor surveillance but also increasingly affected cancer management in those patients. Therefore, the AACR Cancer Predisposition Working Group chose to highlight these advances in CNS tumor predisposition and summarize and/or generate surveillance recommendations for established and more recently emerging pediatric brain tumor predisposition syndromes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".