Management of aortic disease in children with <i>FBN1</i>-related Marfan syndrome
Bibliographic record
Abstract
Marfan syndrome (MFS) is a hereditary connective tissue disorder with an estimated prevalence of 1:5000-1:10 000 individuals. It is a pleiotropic disease characterized by specific ocular, cardiovascular, and skeletal features. The most common cardiovascular complication is aortic root dilatation which untreated can lead to life-threatening aortic root dissection, mainly occurring in adult patients. Prompt diagnosis, appropriate follow-up, and timely treatment can prevent aortic events. Currently there are no specific recommendations for treatment of children with MFS, and management is greatly based on adult guidelines. Furthermore, due to the scarcity of studies including children, there is a lack of uniform treatment across different centres. This consensus document aims at bridging these gaps of knowledge. This work is a joint collaboration between the paediatric subgroup of the European Network of Vascular Diseases (VASCERN, Heritable Thoracic Aortic Disease Working Group) and the Association for European Paediatric and Congenital Cardiology (AEPC). A group of experts from 12 different centres and 8 different countries participated in this effort. This document reviews four main subjects, namely, (i) imaging of the aorta at diagnosis and follow-up, (ii) recommendations on medical treatment, (iii) recommendations on surgical treatment, and (iv) recommendations on sport participation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".