A review of drug patch testing and implications for HIV clinicians
Bibliographic record
Abstract
Systemic drug-induced hypersensitivity reactions (HSRs) have been reported for a variety of drugs, and are thought to have a combined immunological, genetic and metabolic basis. These diverse idiosyncratic reactions are both drug and host dependent, and subsequent rechallenge with the drugs responsible can result in a potentially life-threatening clinical reaction.Hypersensitivity has been observed in regard to several drugs used to manage HIV and associated infections, with the antiretrovirals nevirapine and abacavir being the best characterized of the syndromes. These events represent a high cost both to the patient and the healthcare system, and those labelled as being hypersensitive to one or both drugs may find their treatment options significantly reduced.The identification of HSRs can be challenging due to the heterogeneity of their clinical manifestations. Furthermore, with multidrug regimens - common in HIV management - it may be difficult to identify the drug responsible. Epicutaneous patch testing, a procedure well established in contact dermatitis, has also been used as a supplementary diagnostic test for several drug-related HSRs; its usefulness, however, depends on both the drug and syndrome involved. This study discusses HSRs and the application of patch testing to their investigation, with particular emphasis on HIV and abacavir - the antiretroviral with which patch testing has been most studied
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".