Patch Testing Ingredients of Dermabond and Other Cyanoacrylate-Containing Adhesives
Bibliographic record
Abstract
BACKGROUND: Cyanoacrylates are strong adhesives used for a variety of medical, industrial, and cosmetic applications and have been implicated as a cause of allergic contact dermatitis. OBJECTIVE: The aim of the study was to review our experience in patch testing with cyanoacrylates. METHODS: We reviewed patch test results of 38 patients with a clinical history of contact dermatitis due to a cyanoacrylate-containing adhesive (mostly Dermabond). Testing used cyanoacrylates of >99% purity diluted to 10% to 30% in petrolatum (pet.), undiluted octyl cyanoacrylate, and/or Dermabond Mini or Advanced "as is." Patch tests were also performed with methacrylates, formaldehyde (a cyanoacrylate impurity), benzalkonium chloride, and cyanoacrylate polymerization inhibitors. Three patients were also tested with Dermabond Mini on abraded skin. RESULTS: Commercial cyanoacrylate patch testing material (ethyl cyanoacrylate 10% pet.) detected 29% of Dermabond-allergic patients, whereas patch testing with octyl cyanoacrylate 10% pet. increased detection to 50%. Testing with higher concentrations and/or on abraded skin further increased yield. Thirteen (37%) of our 35 cyanoacrylate-allergic patients were also allergic to methacrylates or acrylates. CONCLUSIONS: Octyl cyanoacrylate is the usual allergenic ingredient in Dermabond. Patch testing with high concentrations is often required. Testing Dermabond on abraded skin further improves diagnostic sensitivity by more closely simulating clinical use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".