Prevalence of Evaluation for Latex Allergy and Association With Practice Characteristics in United States Dermatologists: Results of a Cross-Sectional Survey
Bibliographic record
Abstract
Background: Natural rubber latex allergy is a potentially life-threatening, immunoglobin E (IgE) mediated reaction. Despite great strides in identification of high-risk groups, methods for diagnosis remain limited in the United States and most evaluations are performed by allergists. Objective: The objective of this study was to estimate the prevalence of evaluation for latex allergy and association with practice characteristics in United States dermatologists. Methods: A cross-sectional survey of one third of United States Fellows of the American Academy of Dermatology. Results: The survey response rate was 43%. Of responding dermatologists, 17% stated that they evaluate patients for latex allergy, most commonly with a radioallergosorbent (RAST) or use test. Only 3.6% stated that they perform prick or scratch tests for latex allergy in their office, and most of these dermatologists (86%) prepare their own latex prick test solutions. Evaluation for latex allergy was significantly associated with patch testing, photopatch testing, an interest in contact dermatitis, and number of contact dermatitis books owned, but not with number of years in practice. Conclusions: Most United States dermatologists do not evaluate patients for latex allergy, most likely because of lack of available antigens and because methods for diagnosing latex allergy are not familiar to most dermatologists.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".