A rare case of IgG4-related aortitis in the thoracic aorta mimicking an intramural hematoma: navigating the diagnostic labyrinth
Bibliographic record
Abstract
A 54-year-old female presented with recurrent abdominal pain and new onset chest pain. Chest computed-tomography angiogram detected a thoracic aortic aneurysm with suspected Type A intramural hematoma (IMH) versus aortitis. Initially, conservative management was pursued while awaiting a definitive diagnosis. Differential workup was negative, while additional imaging modalities favored IMH, prompting expedited surgical intervention. During ascending aortic and hemiarch replacement, severe aortitis was unexpectedly discovered without evidence of IMH. Histopathological examination of the aortic specimens identified transmural aortic inflammation with lymphoplasmacytic infiltrate and irregular fibrosis. Numerous IgG4-positive plasma cells were present with IgG4/IgG ratio of 40-50% suggesting IgG4-related disease (IgG4-RD). Subsequent analysis revealed B cells positive for clonal IgH gene rearrangement, and bone marrow biopsy then revealed the same clonal B cells. She was ultimately diagnosed with CLL, the most common phenotype of monoclonal B-cell lymphocytosis, thought to account for the IgG4-predominant plasma cells causing aortitis. Although rare, this case highlights the importance of considering IgG4-related disease (IgG4-RD) as a cause of aortitis when assessing symptomatic patients with aortic pathologies, emphasizing the complexities involved in diagnosing due to a variety of imaging presentation, differentiating, and managing large-vessel vasculitides. Moreover, it underscores the importance of Multidisciplinary Aortic Team care and the use of multiple diagnostic modalities in evaluating ambiguous aortic pathologies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".