Neurovascular Manifestations in Hereditary Hemorrhagic Telangiectasia: Imaging Features and Genotype-Phenotype Correlations
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Hereditary hemorrhagic telangiectasia is an autosomal dominant disease that presents in 10%-20% of patients with various brain vascular malformations. We aimed to report the radiologic features (phenotype) and the genotype-phenotype correlations of brain vascular malformations in hereditary hemorrhagic telangiectasia. MATERIALS AND METHODS: Demographic, clinical, genotypic, and imaging information of 75 patients with hereditary hemorrhagic telangiectasia with brain arteriovenous malformations enrolled in the Brain Vascular Malformation Consortium from 2010 to 2012 were reviewed. RESULTS: Nonshunting, small, superficially located conglomerates of enhancing vessels without enlarged feeding arteries or draining veins called "capillary vascular malformations" were the most commonly observed lesion (46 of 75 patients; 61%), followed by shunting "nidus-type" brain AVMs that were typically located superficially with a low Spetzler-Martin Grade and a small size (32 of 75 patients; 43%). Direct high-flow fistulous arteriovenous shunts were present in 9 patients (12%). Other types of vascular malformations (dural AVF and developmental venous anomalies) were present in 1 patient each. Multiplicity of vascular malformations was seen in 33 cases (44%). No statistically significant correlation was observed between hereditary hemorrhagic telangiectasia gene mutation and lesion type or lesion multiplicity. CONCLUSIONS: Depending on their imaging features, brain vascular malformations in hereditary hemorrhagic telangiectasia can be subdivided into brain AVF, nidus-type AVM, and capillary vascular malformations, with the latter being the most common phenotype in hereditary hemorrhagic telangiectasia. No genotype-phenotype correlation was observed among patients with this condition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".