Mosaic Neurofibromatosis Type 1 in Children: A Single-Institution Experience
Bibliographic record
Abstract
BACKGROUND: Neurofibromatosis type 1 (NF1) is a neurocutaneous disorder caused by loss-of-function mutation in the NF1 gene. Segmental or mosaic NF1 (MNF) is an uncommon presentation of the NF1 result of postzygotic mutations that present with subtle localised clinical findings. OBJECTIVES: Our study's objectives were to describe the clinical characteristics of children with MNF. METHODS: We conducted a cross-sectional study of children diagnosed with MNF at the Hospital for Sick Children in Toronto, Canada, from January 1992 to September 2012. Data were abstracted from health records and analysed using a standardised data collection form approved by our hospital Research Ethics Board. RESULTS: We identified 60 patients with MNF; 32 of 60 (53.3%) were female. Mean ± SD age at first assessment was 10.6 ± 4.6 years. The most common initial physical manifestation in 39 of 60 (65.0%) patients was localised pigmentary changes only, followed by plexiform neurofibromas only in 10 of 60 (16.7%) and neurofibromas only in 9 of 60 (15.0%). Unilateral findings were seen in 46 of 60 (76.7%) patients. Most common associations identified included learning disabilities (7/60; 12%) and bony abnormalities (6/60; 10.0%). CONCLUSIONS: MNF is an underrecognised condition with potential implications for patients. Children mostly present with pigmentary anomalies only. Most patients do not develop associated findings or complications before adulthood, but long-term follow-up will help determine outcomes and possible associations. Recognition and confirmation of the diagnosis is important to provide follow-up and genetic counselling to patients.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".