Prevalence of Patch Testing and Methodology of Dermatologists in the U.S.: Results of a Cross-Sectional Survey
Bibliographic record
Abstract
Background: Patch testing is considered to be the standard for diagnosis of delayed-type hypersensitivity reactions of the skin (allergic contact dermatitis). Objective: The objective of this study was to examine the prevalence of patch testing by US dermatologists and associated practice characteristics. Methods: One-third of US Fellows of the American Academy of Dermatology were sampled systematically with a written survey. Responses from this survey were compared with responses from a 1990 survey of dermatologists. Results: Eighty-three percent of responding dermatologists stated that they performed patch testing in their practice. Whereas the majority of patch testing dermatologists (52%) used a 48-hour, 96-hour patch test reading schedule, 26% performed a single reading at 48 or 72 hours. Among patch testing dermatologists, most (74%) used TRUE Test, and many (44%) did so because it was less time consuming for staff. Many dermatologists (46%) felt that they were patch testing more patients now than when TRUE Test was not available. Eleven percent of dermatologists who patch tested also photopatch tested. Conclusions: The proportion of US dermatologists who patch test has significantly increased from 1990 to 1997 (P< .0001). Whereas the majority of US dermatologists patch test, one quarter of those who do so perform only a single reading.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".