Use of cardiovascular magnetic resonance in the evaluation of a giant right atrial appendage aneurysm: a case report and review of the literature
Bibliographic record
Abstract
BACKGROUND: Right atrial appendage aneurysms are rare entities that may have significant clinical consequences. When co-existing with atrial fibrillation, patients are at risk of developing pulmonary or paradoxical systemic emboli. CASE PRESENTATION: An elderly patient presented to medical attention with symptoms of acute diverticulitis. On abdominal computed tomography, a massively enlarged right atrial appendage aneurysm was discovered incidentally. The aneurysm caused marked compression of the right ventricle and contained an area of hypoenhancement concerning for an intraluminal thrombus. Gadolinium-enhanced cardiovascular magnetic resonance was performed and first-pass perfusion images demonstrated that the area of hypoenhancement was in fact poorly mixing blood. The patient was therefore managed medically. CONCLUSION: Right atrial appendage aneurysms are infrequently encountered cardiac abnormalities. In the literature, surgery has been offered to patients who are young, symptomatic, or have evidence of thrombotic disease, although whether this practice pattern is associated with superior clinical outcomes is unclear. In the present case, gadolinium-enhanced cardiovascular magnetic resonance imaging was used to exclude the presence of intraluminal thrombus in an elderly patient, which helped orient the patient's treating team towards medical-rather than surgical-therapy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".