Approach to allergic contact dermatitis caused by topical medicaments
Bibliographic record
Abstract
OBJECTIVE: To provide an approach to identifying topical medicament ingredients that cause allergic contact dermatitis (ACD) and to recognizing common clinical scenarios in which these ingredients might present. SOURCES OF INFORMATION: A retrospective chart review was conducted of patients patch tested at the Contact Dermatitis Clinic at St Paul's Hospital in Vancouver, BC, between November 2016 and June 2019. Data from the North American Contact Dermatitis Group from 2015 to 2016 and The Ottawa Hospital patch test clinic from 2000 to 2010 were also reviewed. MAIN MESSAGE: Topical antibiotics are the most common cause of ACD to medicaments and frequently cause cosensitization to multiple allergens. This hypersensitivity reaction is often seen following surgical procedures and should be distinguished from postoperative infection. Corticosteroid allergy is easy to miss and should be suspected in cases of corticosteroid-sensitive dermatoses that worsen despite appropriate treatment. Topical anesthetics and propylene glycol are other causes of ACD found in many prescription and over-the-counter products. CONCLUSION: Allergic contact dermatitis is easy to miss and should always be considered in cases of eczematous eruptions. A thorough drug history including all topical products-both prescription and over-the-counter-is critical. Patch testing can help identify specific allergens for the patient to avoid.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".