Patch-Test Reactions to Topical Anesthetics: Retrospective Analysis of Cross-Sectional Data, 2001 to 2004
Bibliographic record
Abstract
BACKGROUND: Allergy to topical anesthetics is not uncommon. The cross-reactivity among topical anesthetics and the screening value of benzocaine alone are not well understood. OBJECTIVES: The goals for this study were: (1) to evaluate the frequency and pattern of allergic patch-test reactions to topical anesthetics, using North American Contact Dermatitis Group (NACDG) data, and (2) to compare these results to allergen frequencies from other published studies. METHODS: The NACDG patch-tested 10,061 patients between 2001 and 2004. In this analysis patients were included who had positive patch-test reactions to one or more of the following: benzocaine, lidocaine, dibucaine, tetracaine, and prilocaine. RESULTS: Of patch-tested patients, 344 (3.4%) had an allergic reaction to at least one anesthetic. Of those, 320 (93.0%) had an allergic reaction to only one topical anesthetic. Overall, reactions to benzocaine (50.0%, 172 of 344) were most prevalent, followed by reactions to dibucaine (27.9%, 96 of 344); however, reactions to dibucaine were significantly more frequent in Canada than in the United States (relative risk [RR], 2.31; 95% confidence interval [CI], 1.67-3.20; p < .0001). Of patients reacting to more than one anesthetic, most (79%, 19 of 24) reacted to both an amide and an ester. CONCLUSIONS: Of the topical anesthetics tested, benzocaine was the most frequent allergen overall. Over 50% of allergic reactions to topical anesthetics in this study would have been missed had benzocaine been used as a single screening agent. Cross-reactivity patterns were not consistent with structural groups.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".