Patch Testing in Non-Immediate Drug Eruptions
Bibliographic record
Abstract
: The present review addresses the literature regarding the sensitivity and specificity of the various diagnostic methods for evaluating non-immediate (ie, occurring more than 1 hour after drug administration) hypersensitivity reactions associated with beta-lactams and other antibiotics, anticonvulsants, heparins, iodinated contrast media, etc. Such reactions include several clinical entities, which range from mild reactions, such as maculopapular rash and delayed-appearing urticaria, to severe ones, such as acute generalized exanthematous pustulosis (AGEP), Stevens-Johnson syndrome, and toxic epidermal necrolysis (TEN). Clinical and laboratory studies indicate that a cell-mediated pathogenic mechanism is often involved in maculopapular rashes. However, this mechanism has also been demonstrated in other non-immediate reactions, such as urticarial and/or angioedematous manifestations, TEN, bullous exanthems, and AGEP. Patch tests, together with delayed-reading intradermal tests, lymphocyte transformation tests, and challenges, are useful tools for evaluating non-immediate drug eruptions. Patch tests can be performed with any form of commercial drugs and are safer than intradermal tests. However, patch tests are less sensitive than intradermal tests, and their sensitivity may vary, depending on the vehicle used.
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".