Use of the National Institutes of Health Criteria for Diagnosis of Neurofibromatosis 1 in Children
Bibliographic record
Abstract
OBJECTIVE: The National Institutes of Health (NIH) Diagnostic Criteria for neurofibromatosis 1 (NF1) are very useful clinically, but some individuals who are later shown to have NF1 cannot be diagnosed in early childhood using these criteria. The aim of this study is to determine the value of the NIH Diagnostic Criteria for NF1 in early childhood, to determine the age at which diagnosis can confidently be made, and to clarify the age at onset of the cardinal clinical features used in the NIH Diagnostic Criteria. METHODS: We studied 1893 NF1 patients under 21 years old from the National Neurofibromatosis Foundation International Database to determine the age at which the features included in the NIH Diagnostic Criteria appear. RESULTS: Approximately 46% of sporadic NF1 cases fail to meet the NIH Diagnostic Criteria by 1 year of age. Nearly all (97%; 95% confidence interval: 94-98) NF1 patients meet the criteria for diagnosis by 8 years old, and all do so by 20 years old. The usual order of appearance of the clinical features listed as NIH criteria is café-au-lait macules, axillary freckling, Lisch nodules, and neurofibromas. Symptomatic optic glioma is usually diagnosed by 3 years old, and characteristic osseous lesions are usually apparent within the first year of life. CONCLUSION: The diagnosis of NF1 cannot always be made in young children using the NIH Diagnostic Criteria. Modification of these criteria may be necessary for children under 8 years old.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".