Assessment of arterial damage in vascular Ehlers-Danlos syndrome: A retrospective multicentric cohort
Bibliographic record
Abstract
Background Vascular Ehlers-Danlos syndrome (vEDS) is a rare inherited connective tissue disorder due to pathogenic variants in COL3A1 leading to medium-size-artery (MSA) dissection, aneurysm, rupture. Aortic lesions are rarer and less investigated. The objective was to describe the distribution of MSA and aortic lesions and the type of COL3A1 variants in a multicentric cohort of 330 adult vEDS patients. Methods At the time of the study, 87% were alive, 60.3% were index cases, and 60.0% were women. COL3A1 variants were identified using NGS and/or Sanger sequencing and classified according to functional consequences: 80.6% leading to dominant-negative (DN) and 19.4% leading to haploinsufficiency (HI). Imaging was systematically performed during the initial workup. Carotid mechanics were assessed by echo tracking in a subgroup of patients. Results Arterial lesions were reported in 82.4% of the patients (N = 272): 83.5% had MSA lesions alone, 3.3% had aortic lesions alone, and 13.2% both. DN variants were associated with a higher prevalence of arterial lesions (P < 0.044), especially in supra-aortic trunks and renal arteries. The prevalence of aortic lesions in HI patients with arterial lesions was higher than that in patients with DN (P 0.027), but not anymore when adjusted for age (P < 0.559). Carotid Young’s modulus was lower in patients with DN, in association with the higher incidence of MSA lesions in this group. Conclusion The prevalence of aortic lesions is not influenced by the COL3A1 genotype when adjusted for age. Patients with DN variant vEDS have a higher frequency of MSA lesions, especially in supra-aortic trunks associated with lower carotid stiffness. These results support optimized care and follow-up for these vulnerable patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".