Multi-organ embolism caused by oscillating aortic valve vegetation
Bibliographic record
Abstract
INTRODUCTION: Valvular vegetation is often due to rheumatic heart disease and infective endocarditis. However, multi-arterial embolism can happen in older patients with no history of infection, fever, and cardiac symptoms. We describe a case of multi-organ embolism caused by oscillating aortal valve vegetation. PATIENT CONCERNS: An 80-year-old woman without a history of infection, fever, and heart symptoms showed sudden loss of consciousness and symptoms of a multi-vessel embolism. Magnetic resonance imaging revealed multiple patchy ischemic foci in both cerebral hemispheres in the same time-phase, and echocardiography showed regurgitation in the aortic valve due to an abnormally hypo-hyperechoic mass measuring about 7.7 × 17.2 mm and oscillating aortic valve vegetation, which was induced by cardiac contraction. DIAGNOSIS: Multiple organ cardiac embolisms caused by oscillating aortic valve vegetation. INTERVENTIONS: Anti-platelet, fluid-supplement, and vascular-dilating therapies as well as intravenous diazepam were given to the patient. OUTCOME: The patient died of epileptic attack secondary to the cerebral embolism. CONCLUSIONS: The patient's whole-body multi-vessel ischemic events in nearly the same time-phase should have encouraged us to consider the possibility of cardiogenic embolism and thus early examination and treatment, although she was old with a relatively poor response due to early infection and physical discomfort. Clinicians should be aware that aortic valve vegetation induces generalized multi-organ embolism in the setting of infective endocarditis in order to ensure prompt recognition and treatment of this fatal complication.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".