Skin manifestations following anti‐COVID‐19 vaccination: A multicentricstudy from Turkey
Bibliographic record
Abstract
PURPOSE: After the emergence of the pandemic caused by the COVID-19 virus, vaccination with various vaccines has started to be implemented across the world. To identify dermatological reactions developing after the COVID-19 vaccines administered in Turkey and determine their clinical features and risk factors that may play a role in their development. MATERIALS AND METHODS: The study included patients aged ≥18 years, who presented to 13 different dermatology clinics in Turkey between July 2021 and September 2021 after developing dermatological reactions following the administration of the COVID-19 vaccine. After providing written consent, the patients were asked to complete a standard survey including questions related to age, gender, occupation, comorbidities, the regular medication used, the onset of cutaneous reactions after vaccination, and localization of reactions. Dermatological reactions were categorized according to whether they developed after the first or second dose of the vaccine or whether they occurred after the inactivated or messenger RNA (mRNA) vaccine. The relationship between dermatological reactions and some variables such as gender and comorbidities was also evaluated. RESULTS: A total of 269 patients [116 women (43.1%), 153 men (56.9%)] were included in the study. It was observed that the dermatological diseases and reactions that most frequently developed after vaccination were urticaria (25.7%), herpes zoster (24.9%), maculopapular eruption (12.3%), and pityriasis rosea (4.5%). The rate of dermatological reactions was 60.6% after the administration of the mRNA vaccine and 39.4% after that of the inactivated vaccine. There was a statistically significantly higher number of reactions among the patients that received the mRNA vaccine (p = 0.001). CONCLUSION: The most common reactions in our sample were urticaria, herpes zoster, and maculopapular eruption. Physicians should know the dermatological side effects of COVID-19 vaccines and their clinical features.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".