Cancer and Central Nervous System Tumor Surveillance in Pediatric Neurofibromatosis 1
Bibliographic record
Abstract
Abstract Although the neurofibromatoses consist of at least three autosomal dominantly inherited disorders, neurofibromatosis 1 (NF1), neurofibromatosis 2 (NF2), and schwannomatosis, NF1 represents a multisystem pleiotropic condition very different from the other two. NF1 is a genetic syndrome first manifesting in childhood; affecting multiple organs, childhood development, and neurocognitive status; and presenting the clinician with often complex management decisions that require a multidisciplinary approach. Molecular genetic testing (see article for detailed discussion) is recommended to confirm NF1, particularly in children fulfilling only pigmentary features of the diagnostic criteria. Although cancer risk is not the major issue facing an individual with NF1 during childhood, the condition causes significantly increased malignancy risks compared with the general population. Specifically, NF1 is associated with highly elevated risks of juvenile myelomonocytic leukemia, rhabdomyosarcoma, and malignant peripheral nerve sheath tumor as well as substantial risks of noninvasive pilocytic astrocytoma, particularly optic pathway glioma (OPG), which represent a major management issue. Until 8 years of age, clinical assessment for OPG is advised every 6 to 12 months, but routine MRI assessment is not currently advised in asymptomatic individuals with NF1 and no signs of clinical visual pathway disturbance. Routine surveillance for other malignancies is not recommended, but clinicians and parents should be aware of the small risks (<1%) of certain specific individual malignancies (e.g., rhabdomyosarcoma). Tumors do contribute to both morbidity and mortality, especially later in life. A single whole-body MRI should be considered at transition to adulthood to assist in determining approaches to long-term follow-up. Clin Cancer Res; 23(12); e46–e53. ©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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.001 |
| 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".