HLA-B*5801 Should Be Used to Screen for Risk of Stevens-Johnson Syndrome in Family Members of Han Chinese Patients Commencing Allopurinol Therapy
Bibliographic record
Abstract
To the Editor: Allopurinol is the major drug used in the treatment of gout and hyperuricemia. Generally, the drug is well tolerated, although a minority of people, about 2%, develop a hypersensitivity reaction with rash or, less frequently, Stevens-Johnson syndrome (SJS)1. A multinational study reported that allopurinol is the most common drug associated with SJS and toxic epidermal necrolysis2. Genomic studies have shown that the HLA-B*5801 allele is a strong risk factor (OR 34–348) for developing allopurinol-induced SJS3,4,5,6,7,8. However, the clinical utility of HLA-B*5801 is unclear. We conducted an observational study in the immediate family members of a male patient who experienced allopurinol-induced SJS in 1997 (index case). The patient, now 72 years old, was diagnosed with SJS by a dermatologist when he developed a generalized blistering rash, fever, and internal organ failure … Address correspondence to Dr. R.O. Day, Clinical Pharmacology, St. Vincent’s Hospital Sydney, Darlinghurst, New South Wales 2010, Australia. E-mail: R.Day{at}unsw.edu.au
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".