Associations of clinical features in neurofibromatosis 1 (NF1)
Bibliographic record
Abstract
Neurofibromatosis 1 (NF1), an autosomal dominant disease, exhibits extreme clinical variability. This variability greatly increases the burden for affected families and impairs our ability to understand the pathogenesis of NF1. Recognition of heterogeneity within a disease may provide important pathogenic insights, therefore we tested clinical data from three large sets of NF1 patients for evidence that certain common features are more likely to occur in some NF1 patients than in others. Clinical information on 4,402 patients with NF1 was obtained from three independent databases. We examined associations between pairs of clinical features in individual affected probands. We also examined associations between the occurrence of individual features in affected relatives. Associations were summarized as odds ratios with 95% confidence intervals. We found associations between several pairs of features in affected probands: intertriginous freckling and Lisch nodules, discrete neurofibromas and plexiform neurofibromas, discrete neurofibromas and Lisch nodules, plexiform neurofibromas and scoliosis, learning disability or mental retardation and seizures. We also found associations between the occurrence of Lisch nodules, macrocephaly, short stature, and learning disability or mental retardation as individual features in parents and children with NF1. Our observations suggest that, contrary to established belief, some NF1 patients are more likely than others to develop particular manifestations of the disease. Genetic factors appear to determine the development of particular phenotypic features.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".