Genetic predisposition of life-threatening antiepileptic-induced skin reactions
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Recent advances in pharmacogenetic studies have uncovered increasingly more genes that predispose individuals to adverse drug reactions. Aromatic antiepileptic drugs (AEDs) are a frequent cause of severe cutaneous adverse reactions (SCAR). A strong genetic association between HLA-B*1502 and carbamazepine (CBZ)-induced Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) has been shown in Han Chinese patients. AREAS COVERED IN THIS REVIEW: This article reviews and updates genetic information associated with CBZ and other AEDs causing SCAR in different ethnic populations. WHAT THE READER WILL GAIN: Independent studies from different countries confirmed that patients carrying the HLA-B*1502 are at high risk of SJS/TEN when exposed to CBZ. The US FDA and similar regulatory agencies in Canada and Taiwan have updated the CBZ drug label to include the genetic information. Available data also suggest that HLA-B*1502 is a risk allele for SJS/TEN caused by other aromatic AEDs with a similar structure to CBZ. TAKE HOME MESSAGE: Screening for HLA-B*1502 allele before starting treatment with CBZ is justified in patients from high-risk populations as recommended by regulatory agencies. Similar chemicals should also be avoided in individuals who test positive for HLA-B*1502.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".