Intrafamilial correlation of clinical manifestations in neurofibromatosis 2 (NF2)
Bibliographic record
Abstract
Measuring correlation in clinical traits among relatives is important to our understanding of the causes of variable expressivity in Mendelian diseases. Random effects models are widely used to estimate intrafamilial correlations, but such models have limitations. We incorporated survival techniques into a random effects model so that it can be used to estimate intrafamilial correlations in continuous variables with right censoring, such as age at onset. We also describe a negative-binomial gamma mixture model to determine intrafamilial correlations of discrete (e.g., count) data. We demonstrate the utility of these methods by analyzing intrafamilial correlations among patients with neurofibromatosis 2 (NF2), an autosomal-dominant disease caused by mutations of the NF2 tumor-suppressor gene. We estimated intrafamilial correlations in age at first symptom of NF2, age at onset of hearing loss, and number of intracranial meningiomas in 390 NF2 nonprobands from 153 unrelated families. A significant intrafamilial correlation was observed for each of the three features: age at onset (0.35; 95% confidence interval (CI) 0.23-0.47), age at onset of hearing loss (0.51; 95% CI, 0.35-0.64), and number of meninginomas (0.29; 95% CI, 0.15-0.43). Significant correlations were also observed for age at first symptom within NF2 families with truncating mutations (0.41; 95% CI, 0.06-0.68) or splice-site mutations (0.29; 95% CI, 0.03-0.51), for age at onset of hearing loss within families with missense mutations (0.67; 95% CI, 0.18-0.89), and for number of meningiomas within families with splice-site mutations (0.39; 95% CI, 0.13-0.66). Our findings are consistent with effects of both allelic and nonallelic familial factors on the clinical variability of NF2.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".