Analysis of intrafamilial phenotypic variation in neurofibromatosis 1 (NF1)
Bibliographic record
Abstract
The relationship of genetic factors to variable expressivity in neurofibromatosis 1 (NF1) is poorly understood. We examined familial aggregation of NF1 features among different classes of affected relatives. Clinical information was obtained from the National NF Foundation International Database on 904 affected individuals in 373 families with 2 or more members with NF1. We used multivariate probit regression to measure the associations between various classes of relatives for each of 10 clinical features of NF1, while simultaneously adjusting for covariates including related features, age, and gender. Two distinct patterns were observed when we compared associations between first- and second-degree relatives, sibs, and parent-child pairs: Lisch nodules and café-au-lait spots had greater associations between first-degree relatives than between second-degree relatives, while subcutaneous neurofibromas, plexiform neurofibromas, café-au-lait spots, and intertriginous freckling had greater associations between sibs than between parents and children. In addition, Lisch nodules, subcutaneous neurofibromas, and cutaneous neurofibromas had greater associations between affected fathers and children than between affected mothers and children. These familial patterns suggest that unlinked modifying genes and the normal NF1 allele may both be involved in the development of particular clinical features of NF1, but that the relative contributions vary for different 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".