Drug eruptions: approaching the diagnosis of drug-induced skin diseases.
Bibliographic record
Abstract
Adverse drug reactions are a major problem in drug therapy, and cutaneous drug reactions account for a large proportion of all adverse drug reactions. Cutaneous drug reactions are also a challenging diagnostic problem since they can mimic a large variety of skin diseases, including viral exanthema, collagen vascular disease, neoplasia, bacterial infection, psoriasis, and autoimmune blistering disease, among others. Furthermore, determining that a particular medication caused an eruption is often difficult when the patient is taking multiple drugs. In this review, we will describe and illustrate a thoughtful, comprehensive, and clinical approach to the diagnosis and management of adverse cutaneous drug reactions. A morphologic approach to drug eruption includes those that are classified as maculopapular, urticarial, blistering or pustular with or without systemic manifestations. Exanthematous drug eruptions, drug hypersensitivity syndrome, urticaria and angioedema, serum sickness-like reactions, fixed drug eruptions, drug-induced autoimmune blistering diseases, Stevens-Johnson syndrome, toxic epidermal necrolysis, drug-induced acne, acute generalized exanthematous pustulosis, lichenoid drug eruptions and photosensitivity eruptions will be discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".