Updated diagnostic criteria and nomenclature for neurofibromatosis type 2 and schwannomatosis: An international consensus recommendation
Bibliographic record
Abstract
PURPOSE: Neurofibromatosis type 2 (NF2) and schwannomatosis (SWN) are genetically distinct tumor predisposition syndromes with overlapping phenotypes. We sought to update the diagnostic criteria for NF2 and SWN by incorporating recent advances in genetics, ophthalmology, neuropathology, and neuroimaging. METHODS: We used a multistep process, beginning with a Delphi method involving global disease experts and subsequently involving non-neurofibromatosis clinical experts, patients, and foundations/patient advocacy groups. RESULTS: We reached consensus on the minimal clinical and genetic criteria for diagnosing NF2 and SWN. These criteria incorporate mosaic forms of these conditions. In addition, we recommend updated nomenclature for these disorders to emphasize their phenotypic overlap and to minimize misdiagnosis with neurofibromatosis type 1. CONCLUSION: The updated criteria for NF2 and SWN incorporate clinical features and genetic testing, with a focus on using molecular data to differentiate the 2 conditions. It is likely that continued refinement of these new criteria will be necessary as investigators study the diagnostic properties of the revised criteria and identify new genes associated with SWN. In the revised nomenclature, the term "neurofibromatosis 2" has been retired to improve diagnostic specificity.
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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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".