Genetic predisposition of life-threatening antiepileptic-induced skin reactions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it