Febuxostat Desensitization in a Patient with Previous Stevens-Johnson Syndrome and <i>HLA-B*58:01</i> Genotype
Bibliographic record
Abstract
To the Editor: We describe the first case, to our knowledge, of successful desensitization to febuxostat in a young high-risk patient of Filipino descent with HLA-B*58:01 positive genotype and previous drug-induced Stevens-Johnson Syndrome (SJS), with 2 subsequent episodes of a morbilliform rash with repeat exposures to febuxostat. Drug-induced severe cutaneous adverse reactions (SCAR) include a wide variety of different cutaneous manifestations including SJS, toxic epidermal necrolysis (TEN), drug-induced hypersensitivity syndrome, and drug reaction with eosinophilia and systemic signs (DRESS). Allopurinol, anticonvulsants, and antibiotics are among the most common causes, with allopurinol being the most common trigger. The mortality rate for allopurinol-induced SCAR is as high as 25% in certain genotypes and ethnicities1. The HLA-B*58:01 risk allele is strongly associated with SCAR, with variable prevalence rates in specific ethnicities; most notably in the Asian population1. However, in a metaanalysis of 9 population-control studies, HLA-B*58:01 for detecting allopurinol-induced TEN/SJS was found to be universal in Chinese, Japanese, and white populations2. The 2015 Clinical Pharmacogenetics Implementation Consortium guidelines … Address correspondence to Dr. T. Tana, St. Michael’s Hospital, Division of Allergy and Clinical Immunology, 30 Bond St., Room 8-161, Cardinal Carter Wing, Toronto, Ontario M5B 1W8, Canada. E-mail: t.loo{at}mail.utoronto.ca
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".