Cancer and Central Nervous System Tumor Surveillance in Pediatric Neurofibromatosis 2 and Related Disorders
Bibliographic record
Abstract
Abstract The neurofibromatoses consist of at least three autosomal-dominant inherited disorders: neurofibromatosis type 1 (NF1), neurofibromatosis type 2 (NF2), and schwannomatosis. For over 80 years, these conditions were inextricably tied together under generalized neurofibromatosis. In 1987, the localization of NF1 to chromosome 17q and NF2 (bilateral vestibular schwannoma) to 22q led to a consensus conference at Bethesda, Maryland. The two main neurofibromatoses, NF1 and NF2, were formally separated. More recently, the SMARCB1 and LZTR1 genes on 22q have been confirmed as causing a subset of schwannomatosis. The last 26 years have seen a great improvement in understanding of the clinical and molecular features of these conditions as well as insights into management. Childhood presentation of NF2 (often with meningioma) in particular predicts a severe multitumor disease course. Malignancy is rare in NF2, particularly in childhood; however, there are substantial risks from benign and low-grade central nervous system (CNS) tumors necessitating MRI surveillance to optimize management. At least annual brain MRI, including high-resolution images through the auditory meatus, and a clinical examination and auditory assessment are required from diagnosis or from around 10 to 12 years of age if asymptomatic. Spinal imaging at baseline and every 2 to 3 years is advised with more frequent imaging if warranted on the basis of sites of tumor involvement. The malignancy risk in schwannomatosis is not well defined but may include an increased risk of malignant peripheral nerve sheath tumor in SMARCB1. Imaging protocols are also proposed for SMARCB1 and LZTR1 schwannomatosis and SMARCE1-related meningioma predisposition. Clin Cancer Res; 23(12); e54–e61. ©2017 AACR. See all articles in the online-only CCR Pediatric Oncology Series.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".