Overview of Marfan Syndrome: knowns and unknowns
Bibliographic record
Abstract
Marfan syndrome (MFS) is a relatively rare disease of the connective tissue that affects several organs of the body. Cardiovascular abnormalities such as aortic root dilatation and mitral valve prolapse are the two main life-threatening complications associated with MFS. The complete pathogenesis of MFS is yet unclear. However, fibrillin-1 (FBN1) gene mutations and mutations in the transforming growth factor-β (TGFβ) signaling pathway are the leading causes of this lethal disease. Detailed assessment based on several major and minor clinical manifestations has led to the evolution of different nosologies for MFS diagnoses with reliable accuracies. Nevertheless, heterogeneous disease advancement and overlapping clinical outcomes make MFS diagnosis challenging. Rapid strides in research and surgical avenues over the last two decades have improved the life expectancy and the quality of life of MFS patients remarkably. More specific diagnostic criteria have been established, novel therapeutic targets for pharmacotherapy have been identified and validated, and newer surgical techniques have been tested. Current research efforts are focusing on the identification of prognostic biomarkers, gene modifiers, drug targets, and surgical procedures. This review aims to provide a brief overview of these aspects associated with MFS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".