Global Approach for Severe Cutaneous Adverse Reactions (GRASS) Survey
Bibliographic record
Abstract
BACKGROUND: Consensus guidelines outline the recommended management of delayed drug hypersensitivity reactions (DHRs), but clinical practices are largely expert-based and may vary significantly across populations and regions. This study evaluated the approach and management of health care specialists globally regarding DHRs, including severe cutaneous adverse reactions. OBJECTIVE: To assess the current practices, knowledge, and availability of allergy testing for DHRs, including severe cutaneous adverse reactions, among relevant health care professionals globally. METHODS: The Global AppRoAch for Severe Cutaneous Adverse Reactions Survey study is a prospective, web-based survey conducted using the Research Electronic Data Capture platform. The 10- to 15-minute survey was directed to 14 allergy/immunology and dermatology associations through international mailing lists across 48 countries. RESULTS: Among the 8561 members contacted, answers from 130 health care providers practicing in all 6 continents were analyzed. Most respondents identified as White (88 of 130 [68%]) and female (79 of 130 [61%]), representing a range of age groups and clinical expertise. Most practiced in dermatology (69 of 130 [53%]), followed by allergy/immunology (46 of 130 [35.4%]), and were based in an urban setting (127 of 130 [98%]). Among the 45 respondents (34.6%) who managed severe cutaneous adverse reactions, 27 (60%) had access to multidisciplinary care. Practices varied widely by clinical phenotype; for example, for maculopapular exanthema, 63 respondents indicated treating through the reaction (63 of 130 [49%]), whereas 16 (16 of 130 [12%]) offered desensitization. Under patient education and follow-up, only 60 (60 of 130 [46.2%]) participants organized a follow-up with the implicated specialist. CONCLUSIONS: Significant variability in the management of DHRs was observed among the respondents. The results highlight the need for evidence-based protocols to standardize guidelines for managing DHRs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".