Navigating Aspirin Hypersensitivity in Patients Undergoing Percutaneous Coronary Intervention
Bibliographic record
Abstract
Aspirin hypersensitivity continues to be a major clinical challenge in patients with coronary artery disease (CAD), particularly in those requiring percutaneous coronary intervention (PCI) in the absence of a validated alternative antiplatelet regimen. Although true aspirin allergies are uncommon, they can manifest with severe reactions such as angioedema or anaphylaxis, highlighting the critical role of diagnostic challenge tests and tolerance induction strategies. Here, a 61-year-old female with end-stage renal disease (ESRD) on hemodialysis presented with new-onset heart failure and elevated troponins in the setting of a hypertensive emergency. A subsequent left heart catheterization revealed severe multivessel disease, but PCI was deferred due to her history suggestive of aspirin-induced angioedema and the absence of a known optimal approach in this scenario. Given the feasibility of completing a desensitization protocol, aspirin desensitization was pursued, facilitating the successful placement of a drug-eluting stent. This case highlights the need for validated protocols to manage aspirin hypersensitivity, as the current treatment paradigm necessitates a highly individualized approach by the treating clinician.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".