Multi-Modality Imaging for Accurate Valvular Lesion Diagnosis: A Case Report of Catastrophic Outcomes From Unrecognized Severe Aortic Regurgitation
Bibliographic record
Abstract
Aortic regurgitation (AR) can be difficult to accurately quantify on echocardiography alone, potentially leading to erroneous grading. A 52-year-old male presented following resuscitation after an out-of-hospital cardiac arrest. He had been followed in the cardiology outpatient clinic for a number of years for monitoring of bicuspid aortic valve and associated AR. Regular transthoracic echocardiography and one transesophageal echocardiogram had shown moderate range AR. Cardiac magnetic resonance imaging reported the AR as severe with associated severely dilated left ventricle. Echocardiography grading and the patient's lack of symptoms supported a strategy of active surveillance. The presentation with cardiac arrest prompted re-evaluation of the severity of this patient's AR. Repeat cardiac magnetic resonance imaging re-affirmed severe AR, and the patient proceeded to surgical aortic valve replacement with a bioprosthetic valve. Post-operatively, the patient had heart failure with severely reduced ejection fraction. During hospital stay, he developed thyrotoxicosis secondary to amiodarone. This case demonstrates the discrepancy in assessing severity between different imaging techniques and highlights the potential complications in delayed intervention in AR.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".