Update on Pediatric Cancer Surveillance Recommendations for Patients with Neurofibromatosis Type 1, Noonan Syndrome, CBL Syndrome, Costello Syndrome, and Related RASopathies
Bibliographic record
Abstract
Neurofibromatosis type 1 (NF1), Noonan syndrome, and related syndromes, grouped as RASopathies, result from dysregulation of the RAS-MAPK pathway and demonstrate varied multisystemic clinical phenotypes. Together, RASopathies are among the more prevalent genetic cancer predisposition syndromes and require nuanced clinical management. When compared with the general population, children with RASopathies are at significantly increased risk of benign and malignant neoplasms. In the past decade, clinical trials have shown that targeted therapies can improve outcomes for low-grade and benign neoplastic lesions but have their own challenges, highlighting the multidisciplinary care needed for such individuals, specifically those with NF1. This perspective, which originated from the 2023 American Association for Cancer Research Childhood Cancer Predisposition Workshop, serves to update pediatric oncologists, neurologists, geneticists, counselors, and other health care professionals on revised diagnostic criteria, review previously published surveillance guidelines, and harmonize updated surveillance recommendations for patients with NF1 or RASopathies.
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 | 0.003 | 0.007 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".