Bibliographic record
Abstract
Nevirapine (Viramune TM ) can cause severe and life-threatening idiosyncratic skin rash. No clear mechanistic understanding exists; thus, it is impossible to predict which patients will suffer nevirapine-induced rash or to design a safer nevirapine analogue. Animal models of idiosyncratic drug reactions are rare, but animal models are arguably the key to mechanistic understanding. Recently, an animal model of nevirapine-induced rash was discovered and characterized; nevirapine causes rash in female Brown Norway rats with characteristics akin to nevirapine-induced rash in humans. The animal model has permitted considerable gains in the mechanistic understanding of nevirapineinduced rash, although it has yet to be determined if the animal model findings reflect the pathogenesis of nevirapine- induced rash in humans. Investigations with the animal model confirmed the essential role of the immune system. Ongoing research should determine the relative importance of parent drug versus reactive metabolite as the root cause; this is a fundamental and unanswered question in the field of idiosyncratic drug reactions. Importantly, the animal model of nevirapine-induced rash does not provide a predictive test of the ability of other drugs to cause idiosyncratic reactions, but may provide a sufficient mechanistic understanding of nevirapine-induced rash to allow the prevention of the disease in patients.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.052 | 0.023 |
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".