Genotypes and Phenotypes of 162 Families with a <b><i>Glomulin</i></b> Mutation
Bibliographic record
Abstract
A decade ago, we identified a novel gene, glomulin (GLMN) in which mutations cause glomuvenous malformations (GVMs). GVMs are bluish-purple cutaneous vascular lesions with characteristic glomus cells in the walls of distended venous channels. The discovery of the genetic basis for GVMs allowed the definition of clinical features to distinguish GVMs from other venous anomalies. The variation in phenotype was also highlighted: from a single punctate blue dot to a large plaque-like lesion. In this study, we screened GLMN in a large cohort of patients to broaden the spectrum of mutations, define their frequency and search for possible genotype-phenotype correlations. Taking into account 6 families published by others, a mutation in GLMN has been found in 162 families. This represents 40 different mutations; the most frequent one being present in almost 45% of them. Expressivity varies largely, without a genotype/phenotype relationship. Among 381 individuals with a mutation, we discovered 37 unaffected carriers, implying a penetrance of 90%. As nonpenetrant individuals may transmit the disease to their descendants, knowledge on the mutational status is needed for appropriate genetic counseling.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".