Patient-Reported Association between COVID-19 Infection or Vaccination and Onset of Allergic Contact Dermatitis®
Bibliographic record
Abstract
Abstract: Background: Over the course of the COVID-19 pandemic, our clinic has encountered numerous patients who report that either COVID-19 vaccination or infection was the precipitating event for their development of allergic contact Dermatitis® (ACD). Up to this time, there is no formal investigation regarding COVID-19 vaccination or infection causing ACD. However, there have been several registry-based case series of associated dermatoses after COVID-19 infection or vaccination. Objective: This study aimed to describe patient-reported associations between COVID-19 infection or vaccination and onset of ACD. Methods: A single-center retrospective noncomparative chart review of 1073 patients patch tested at the Park Nicollet Contact Dermatitis® Clinic (Minneapolis, MN) from March 1, 2020, to March 1, 2022, was performed. Results: Of 1073 patients included in our study, 5 patients (0.47%) reported ACD symptom onset after COVID-19 infection and 12 patients (1.11%) reported onset after COVID-19 vaccination. Rates of clinical relevance were not significantly different than the general population for those who attributed ACD to COVID-19 infection or vaccination. Conclusions: To our knowledge, this is the first study in the literature investigating the potential association between COVID-19 vaccination or infection and the development of ACD through extensive retrospective chart review.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".