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Record W4393233718 · doi:10.1089/derm.2023.0379

Patient-Reported Association between COVID-19 Infection or Vaccination and Onset of Allergic Contact Dermatitis®

2024· article· en· W4393233718 on OpenAlexvenueno aff
Nicholas Battis, Samuel F. Ekstein, Eric Cosky, Anne Neeley

Bibliographic record

VenueDermatitis · 2024
Typearticle
Languageen
FieldMedicine
TopicDermatological and COVID-19 studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVaccinationCoronavirus disease 2019 (COVID-19)Retrospective cohort studyPandemicPopulationPediatricsDermatologyImmunologyInternal medicineDisease

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.312
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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