Surgical management for unruptured sinus of Valsalva aneurysms: a narrative review of the literature
Bibliographic record
Abstract
Unruptured sinus of Valsalva aneurysms (SVAs) are rare cardiac lesions that arise due to congenital or acquired etiologies. They could be asymptomatic or cause various clinical manifestations as a consequence of their mass effect on the coronary arteries, heart valves, and other adjacent structures. While the factors predicting SVA rupture are not fully understood, ruptured SVAs carry a high complication and mortality rate, highlighting the need for early recognition and management of unruptured SVAs. Imaging modalities such as echocardiography, computed tomography (CT), angiography, and magnetic resonance imaging (MRI) are essential in identifying and characterizing the aneurysm as well as associated cardiac anomalies. However, there are no specific guidelines for the diagnosis and management of SVAs. Herein, we performed a contemporary systematic review to examine the presentation, diagnostic tests and findings, as well as outcomes for surgical intervention of unruptured SVAs. We demonstrate that surgical repair remains the preferred method of treatment in order to prevent complications such as rupture or thrombus formation. Surgery should be prompted in patients with symptomatic, large, or rapidly expanding unruptured SVAs, as well as those unruptured SVAs that contain intraluminal thrombi, have a mass effect on surrounding structures, or are recurrent. Surgical outcomes are generally good with favourable prognosis and minimal recurrence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".