A novel therapy for an unusual problem: IL‐1 receptor antagonist for recurrent post‐transplant pericarditis
Bibliographic record
Abstract
Heart transplant (HTx) recipients are at increased risk of pericardial disease. Idiopathic recurrent pericarditis has not been previously described following HTx. We describe a 35-year-old male who was admitted with pericarditis and moderate pericardial effusion 10 months after HTx. Two weeks before his admission, his prednisone had been tapered off. A thorough infectious workup and endomyocardial biopsy was unrevealing. He was started on colchicine with the addition of tapering prednisone regimen of 40 mg daily due to unresolved pain. Over the next several years, he had three recurrent episodes of pericarditis requiring re-initiation of prednisone with extensive investigations negative for rejection, autoimmune, and infectious causes. Cardiac MRI confirmed pericardial inflammation. Due to his recurrent course and inability to wean off prednisone, anakinra, an IL-1 receptor antagonist, was started at 100 mg sc daily. This allowed successful discontinuation of prednisone. He is now 34 months post-transplant without recurrence on anakinra and colchicine maintenance. Due to the overlap between idiopathic recurrent pericarditis and auto-inflammatory diseases, there is growing evidence for utilizing IL-1 receptor antagonists in this condition. While pericarditis is common in the HTx population, this is the first report of successful use of an IL-1 receptor blocker for pericarditis in this population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".